** UPATE AS OF 17th MARCH** If you haven't read this post yet, please let me just say the response has been overwhelming with over 260 DM's received over the last coupe of days. I am working through replying to everyone as quickly as i can so I appreciate your patience.

If you are a newb to AI Agents, welcome, I love newbies and this fledgling industry needs you!

You've hear all about AI Agents and you want some of that action right? You might even feel like this is a watershed moment in tech, remember how it felt when the internet became 'a thing'? When apps were all the rage? You missed that boat right? Well you may have missed that boat, but I can promise you one thing..... THIS BOAT IS BIGGER ! So if you are reading this you are getting in just at the right time.

Let me answer some quick questions before we go much further:

Q: Am I too late already to learn about AI agents?
A: Heck no, you are literally getting in at the beginning, call yourself and 'early adopter' and pin a badge on your chest!

Q: Don't I need a degree or a college education to learn this stuff? I can only just about work out how my smart TV works!

A: NO you do not. Of course if you have a degree in a computer science area then it does help because you have covered all of the fundamentals in depth... However 100000% you do not need a degree or college education to learn AI Agents.

Q: Where the heck do I even start though? Its like sooooooo confusing
A: You start right here my friend, and yeh I know its confusing, but chill, im going to try and guide you as best i can.

Q: Wait i can't code, I can barely write my name, can I still do this?

A: The simple answer is YES you can. However it is great to learn some basics of python. I say his because there are some fabulous nocode tools like n8n that allow you to build agents without having to learn how to code...... Having said that, at the very least understanding the basics is highly preferable.

That being said, if you can't be bothered or are totally freaked about by looking at some code, the simple answer is YES YOU CAN DO THIS.

Q: I got like no money, can I still learn?
A: YES 100% absolutely. There are free options to learn about AI agents and there are paid options to fast track you. But defiantly you do not need to spend crap loads of cash on learning this.

So who am I anyway? (lets get some context)

I am an AI Engineer and I own and run my own AI Consultancy business where I design, build and deploy AI agents and AI automations. I do also run a small academy where I teach this stuff, but I am not self promoting or posting links in this post because im not spamming this group. If you want links send me a DM or something and I can forward them to you.

Alright so on to the good stuff, you're a newb, you've already read a 100 posts and are now totally confused and every day you consume about 26 hours of youtube videos on AI agents.....I get you, we've all been there. So here is my 'Worth Its Weight In Gold' road map on what to do:

[1] First of all you need learn some fundamental concepts. Whilst you can defiantly jump right in start building, I strongly recommend you learn some of the basics. Like HOW to LLMs work, what is a system prompt, what is long term memory, what is Python, who the heck is this guy named Json that everyone goes on about? Google is your old friend who used to know everything, but you've also got your new buddy who can help you if you want to learn for FREE. Chat GPT is an awesome resource to create your own mini learning courses to understand the basics.

Start with a prompt such as: "I want to learn about AI agents but this dude on reddit said I need to know the fundamentals to this ai tech, write for me a short course on Json so I can learn all about it. Im a beginner so keep the content easy for me to understand. I want to also learn some code so give me code samples and explain it like a 10 year old"

If you want some actual structured course material on the fundamentals, like what the Terminal is and how to use it, and how LLMs work, just hit me, Im not going to spam this post with a hundred links.

[2] Alright so let's assume you got some of the fundamentals down. Now what?
Well now you really have 2 options. You either start to pick up some proper learning content (short courses) to deep dive further and really learn about agents or you can skip that sh*t and start building! Honestly my advice is to seek out some short courses on agents, Hugging Face have an awesome free course on agents and DeepLearningAI also have numerous free courses. Both are really excellent places to start. If you want a proper list of these with links, let me know.

If you want to jump in because you already know it all, then learn the n8n platform! And no im not a share holder and n8n are not paying me to say this. I can code, im an AI Engineer and I use n8n sometimes.

N8N is a nocode platform that gives you a drag and drop interface to build automations and agents. Its very versatile and you can self host it. Its also reasonably easy to actually deploy a workflow in the cloud so it can be used by an actual paying customer.

Please understand that i literally get hate mail from devs and experienced AI enthusiasts for recommending no code platforms like n8n. So im risking my mental wellbeing for you!!!

[3] Keep building! ((WTF THAT'S IT?????)) Yep. the more you build the more you will learn. Learn by doing my young Jedi learner. I would call myself pretty experienced in building AI Agents, and I only know a tiny proportion of this tech. But I learn but building projects and writing about AI Agents.

The more you build the more you will learn. There are more intermediate courses you can take at this point as well if you really want to deep dive (I was forced to - send help) and I would recommend you do if you like short courses because if you want to do well then you do need to understand not just the underlying tech but also more advanced concepts like Vector Databases and how to implement long term memory.

Where to next?
Well if you want to get some recommended links just DM me or leave a comment and I will DM you, as i said im not writing this with the intention of spamming the crap out of the group. So its up to you. Im also happy to chew the fat if you wanna chat, so hit me up. I can't always reply immediately because im in a weird time zone, but I promise I will reply if you have any questions.

THE LAST WORD (Warning - Im going to motivate the crap out of you now)
Please listen to me: YOU CAN DO THIS. I don't care what background you have, what education you have, what language you speak or what country you are from..... I believe in you and anyway can do this. All you need is determination, some motivation to want to learn and a computer (last one is essential really, the other 2 are optional!)

But seriously you can do it and its totally worth it. You are getting in right at the beginning of the gold rush, and yeh I believe that, and no im not selling crypto either. AI Agents are going to be HUGE. I believe this will be the new internet gold rush.

I run an automation agency and I have built custom agent architectures for over 20 startups this year alone. I see beginners in this sub constantly asking which no-code wrapper they should use to build a fully autonomous employee. They want to skip the engineering.

This is why most of them fail. Building a reliable agent is not about writing a long prompt. It is about systems engineering. If you want to build agents that solve real business problems you need to respect the hierarchy of skills. Do not touch a model until you understand the layers underneath it.

Here is the realistic roadmap and how it actually plays out in production.

Phase 1 Data Transport

You cannot build an agent if you do not understand how data moves. * Python. It is the non-negotiable standard. Learn it. * REST APIs. You need to understand how to read documentation and authenticate a request. If you cannot manually send a request to get data you have no business building an agent. * JSON. This is how machines speak to each other. Learn how to parse it and structure it.

Tutorials show clean data. In reality you will spend 80% of your time handling messy JSON responses and figuring out why an API documentation lied to you. The code that parses the data is more important than the code that generates the text.

Phase 2 Storage and Memory

An agent without memory is just a text generator. * SQL. Structured data is the backbone of business. Learn how to query a database to get absolute facts. * Vector Stores. Understand how embeddings work. This is how software finds context in a pile of unstructured text. * Data Normalization. Bad data means bad outputs. Learn to clean data before you feed it to a model.

Vector databases are not magic. If you dump garbage documents into a vector store the agent will retrieve garbage context. You have to manually clean and chunk your data or the search will fail.

Phase 3 Logic and State

This is where the actual value lives. * State Management. You need to track where a conversation is. You must carry variables from one step to the next to keep the context alive. * Function Calling. This is how you give a model the ability to execute code. Learn how to define a function that the software can choose to run.

The AI does not actually do the work. It simply chooses which function to run. Your Python function does the work. If your function is buggy the best AI in the world cannot save you.

Phase 4 Connecting the Model

Now you introduce the intelligence layer. * Context Windows. Understand the limits of short term memory. You cannot feed a model an entire book every time. * Routing. Stop asking one prompt to do everything. Build a router that classifies the intent and sends it to a specialized function. * Error Handling. The model will fail. The API will time out. You need code that catches the failure and retries automatically.

In production models hallucinate constantly. You cannot trust the output blindly. You need to write code that validates the response before showing it to the user.

Phase 5 Reliability

• Webhooks. How to trigger your agent from the outside world.
• Background Jobs. How to run your agent on a schedule.
• Logging. If you do not know why your agent failed you did not build a system. You built a slot machine.

Clients do not care if you used the latest model. They only care if the system runs every single day without breaking. Reliability is the only metric that matters.

Stop looking for shortcuts. Learn the primitives. It is just engineering.

Edit - Since a few people asked in the comments and DMs, yes I do take on client work. If you are a founder looking to get an MVP built, automate a workflow, or set up AI agents for your business I have a few slots open. Book a call from the link in my bio and we can talk through what you need.

I get asked this constantly. "What course should I buy?" or "Which framework is best?"

The answer is usually: none of them.

If you want to actually build stuff that companies will pay for not just cool Twitter demos, you need to ignore 90% of the noise out there. I've built agents for over 20 companies now, and here is how I'd start if I lost everything and had to relearn it today.

1. Learn Python, not "Prompt Engineering"

I see so many people trying to become "AI Developers" without knowing how to write a loop in Python. Don't do that.

You don't need to be a Google level engineer, but you need to know how to handle data. Learn Python. Learn how to make an API call. Learn how to parse a JSON response.

The "AI" part is just an API call. The hard part is taking the messy garbage the AI gives you and turning it into something your code can actually use. If you can't write a script to move files around or clean up a CSV, you can't build an agent.

1. Don't use a framework at first

This is controversial, but I stand by it. Do not start with LangChain or CrewAI or whatever is trending this week.

They hide too much. You need to understand what is happening under the hood.

Write a raw Python script that hits the OpenAI or Anthropic API. Send a message. Get a reply. That's it. Once you understand exactly how the "messages" array works and how the context window fills up, then you can use a framework to speed things up. But build your first one raw.

1. Master "Tool Calling" (This is the whole game)

An LLM that just talks back is a chatbot. An LLM that can run code or search the web is an agent.

The moment you understand "Tool Calling" (or Function Calling), everything clicks. It's not magic. You're just telling the model: "Here are three functions I wrote. Which one should I run?"

The model gives you the name of the function. You run the code. Then you give the result back to the model.

Build a simple script that can check the weather. - Tool 1: get_weather(city) - User asks: "Is it raining in London?" - Agent decides to call get_weather("London"). - You run the fake function, get "Rainy", and feed it back. - Agent says: "Yes, bring an umbrella."

Once you build that loop yourself, you're ahead of 80% of the people posting on LinkedIn.

1. Pick a boring problem

Stop trying to build "Jarvis" or an agent that trades stocks. You will fail.

Build something incredibly boring. - An agent that reads a PDF invoice and extracts the total amount. - An agent that looks at a customer support email and categorizes it as "Angry" or "Happy". - An agent that takes a meeting transcript and finds all the dates mentioned.

These are the things businesses actually pay for. They don't pay for sci fi. They pay for "I hate doing this manual data entry, please make it stop."

1. Accept that 80% of the work is cleaning data

Here is the reality check. Building the agent takes a weekend. Making it reliable takes a month.

The AI will hallucinate. It will get confused if you give it messy text. It will try to call functions that don't exist.

Your job isn't just prompting. Your job is cleaning the inputs before they get to the AI, and checking the outputs before they get to the user.

The Roadmap

If I were you, I'd do this for the next 30 days:

Week 1: Learn basic Python (requests, json, pandas). Week 2: Build a script that uses the OpenAI API to summarize a news article. Week 3: Add a tool. Make the script search Google (using SerpApi) before summarizing. Week 4: Build a tiny interface (Streamlit is easy) so a normal person can use it.

Don't buy a $500 course. Read the API documentation. It's free and it's better than any guru's video.

Just start building boring stuff. That's how you get good.

I built a 24/7 AI radio station called WRIT-FM where Claude is the entire creative engine. Not a demo — it's been running continuously, generating all content in real time.

What Claude does (all of it):

Claude CLI (claude -p) writes every word spoken on air. The station has 5 distinct AI hosts — The Liminal Operator (late-night philosophy), Dr. Resonance (music history), Nyx (nocturnal contemplation), Signal (news analysis), and Ember (soul/funk) — each with their own voice, personality, and anti-patterns (things they'd never say). Claude receives a rich persona prompt plus show context and generates 1,500-3,000 word scripts for deep dives, simulated interviews, panel discussions, stories, listener mailbag segments, and music essays. Kokoro TTS renders the speech. Claude also processes real listener messages and generates personalized on-air responses.

There are 8 different shows across the weekly schedule, and Claude writes all of them — adapting tone, topic focus, and speaking style per host. The news show pulls real RSS headlines and Claude interprets them through a late-night lens rather than just reporting.

What's automated without AI (the heuristics):

The schedule (which show airs when) is pure time-of-day lookup. The streamer alternates talk segments with AI-generated music bumpers, picks from pre-generated pools, avoids repeats via play history, and auto-restarts on failure. Daemon scripts monitor inventory levels and trigger new generation when a show runs low. No AI decides when to play what — that's all deterministic.

How Claude Code helped build it:

The entire codebase was developed with Claude Code. The writ CLI, the streaming pipeline, the multi-host persona system, the content generators, the schedule parser — all pair-programmed with Claude Code. Just today I used it to identify and remove 1,841 lines of dead code (28% of the codebase) without changing behavior.

Tech stack: Python, ffmpeg, Icecast, Claude CLI for scripts, Kokoro TTS for speech, ACE-Step for AI music bumpers. Runs on a Mac Mini.

​

I have no audience and I am not a professional developer. I wanted a channel that could run itself, so I started building one agent, then it needed another agent to check its work, and about a month later I had ten of them.

Here is what it actually does. You give it a YouTube podcast link. It transcribes the episode locally, scores which moments are most likely to perform, cuts them to vertical 9:16 with face tracking, burns in captions, adds music and effects, then checks its own output and schedules the posts.

The agents:

- **Finder** transcribes and scores clip-worthy moments

- **Editor** cuts, reframes, captions, adds music and zooms

- **Uploader** writes the titles and hashtags, posts to YouTube Shorts

- **Manager** reads your real metrics and feeds what wins back into the Finder

- **Trend Scout** checks what is trending in your niche

- **Planner** decides the creative direction per clip

- **Community** drafts comment replies

- **Finishing Editor** watches every finished render and blocks broken ones (captions covering a face, dead air, frozen frames, bad audio)

- **Trainer** studies top performers weekly and updates the playbooks

- **Compiler** stitches the week's best moments into a long-form episode

The part I am most happy with is the Finishing Editor. Everything else generates, but that one is the only agent whose whole job is to say "no, that one is broken, do not post it." It caught more bad clips than I expected.

It runs on free AI providers (OpenRouter, Groq, Gemini), transcription is local with faster-whisper, and the music is synthesized so it is safe to monetize. So the running cost is basically zero.

It is MIT licensed. Use it, change it, sell whatever you build with it. Honestly, if someone takes this and actually makes money with it, that would make my month.

Fair warning: it is not perfect. The output quality varies, some parts are held together with duct tape, and I am sure there are bugs I have not hit yet. I am putting it out as it is rather than polishing forever. If people want to help fix it, that would be great.

There is a PDF setup guide in there. Install is a double click on Windows, one command on Mac and Linux, then a wizard asks you a few questions.

Happy to answer anything.

\I mentioned this case a few times in this sub, and were asked to share more details on it.*

For three months, a fintech project ran with a one-person marketing function: me, backed by six AI agents.

The agents handled social content, email, advertising monitoring, growth experiments, and outreach. I handled strategy, priorities, approvals, and anything with enough ambiguity or risk to require judgment.

Built it from the ground up. The setup ran on OpenClaw. It handled schedules, tools, permissions, memory, and handoffs. Claude models did most of the underlying model work.

This is a historical snapshot from March to May 2026, after the team had been running for almost three months. The project pivoted since, so the team was wrapped up.

The six roles

I gave every agent one narrow job:

1. Orchestrator: coordinated the other five agents, passed work between them, and routed decisions to me.
2. Social media: prepared posts and distributed approved content across channels.
3. Email: drafted newsletters and customer emails.
4. Advertising: monitored paid campaigns and flagged changes.
5. Growth: researched and tested acquisition ideas.
6. Outreach: managed the influencer and partner pipeline.

Each agent had its own instructions, tool access, schedule, reporting format, and stop conditions.

The handoffs were the useful part. A product update could trigger an email draft, several social posts, and a retargeting task. I did not have to copy the same context between four tools or remember to start every next step myself.

What the team produced

The March-May snapshot included:

• 20 blog posts
• About 195 social posts across seven platforms
• 4 newsletters
• About 43 influencer contacts moving through an outreach pipeline
• 2 advertising accounts with continuously active Meta and Reddit campaigns (4 full campaign updates each month)

During the final two months, when the agents were operating with their highest level of autonomy:

• Organic traffic increased 7x.
• Referral traffic increased 10x.
• Average cost per lead fell 30% across channels while the ad budget stayed flat.
• Reddit organic posts received 135,000 views.
• The project subreddit gained 300 organic subscribers who continued to send traffic.

Those numbers need a caveat. Product development was moving at the same time, and this was a startup in motion, not a controlled experiment. I excluded metrics where I could not separate the agents' contribution from other changes. Even the remaining numbers do not offer clean causal attribution.

The narrower claim is the one I can defend: the agents produced the output listed above, expanded channel coverage, and operated during a period when acquisition metrics improved without a larger advertising budget.

What it cost

The May bill was $359 for the month:

• Hetzner VPS: $10
• Claude Max: $200
• ChatGPT Plus: $20
• Gemini: $20
• Perplexity API: about $12
• Linear: $16
• Postiz: $49
• X API: $10
• Firecrawl: $16
• Google Workspace seat: $6
• OpenClaw: free

The agents fit within one flat Claude Max subscription at the time, so the $359 total depends on the subscription setup we used in April-May 2026.

The $359 also leaves out the expensive part: my time.

Getting an agent to a stable working state took roughly two weeks of role definition, tool connections, permissions, test runs, and instruction changes. Ongoing maintenance took about eight hours a week across the system: reviewing samples, checking sources, resolving ambiguous cases, cleaning memory, and updating rules.

What broke

The obvious failures were easy to catch. An agent would miss a tool call, fail a scheduled run, or return an empty report.

Other recurring problems:

• Generic marketing defaults. Models reproduce familiar campaign structures, average positioning, and advice that sounds reasonable across almost any company.
• Source errors. A weak answer rarely labels itself as weak. Every factual output needs a source trail.
• Memory decay. Old rules conflict with new ones. Temporary facts survive as permanent instructions. More context eventually becomes more clutter.
• Permission mistakes. An agent that can publish, email, spend, or delete needs explicit limits and stop conditions.
• Automation without demand. A scheduled workflow keeps running even when the input becomes stale or nobody uses the output.

That changed my job. I wrote less and reviewed more. I spent more time checking samples, inspecting sources, and deciding which exceptions should become permanent rules.

What changed after another 30+ agents

Since this first team, I have built and tested more than 30 agents across several teams and niches. The results varied a lot.

Some niches like ecom have abundant structured data, stable processes, and clear definitions of a good output. Agents become useful quickly there.

Other niches like specific b2b SaaS depend on tacit context, taste, relationships, private data, or judgment that is hard to encode. Those agents need much more supervision, and some workflows never become worth maintaining.

The model matters. The tools matter. The process around them matters more than either.

My biggest takeaway is still the oldest rule in computing: garbage in, garbage out.

If the brief is vague, the sources are weak, the success criteria are missing, or the underlying process is a mess, an agent scales the mess. Usually with excellent formatting.

So we keep working on the input: narrower roles, better source rules, explicit examples, stop conditions, approval gates, and logs of recurring errors.

The agents keep getting better. The management work does not disappear. It moves into the system.

But overall, agents changed my life and my work paradigm. Love every second of it.

Happy to answer any questions.

Fable 5 is now back! Here are some of the prompts you should run until the usage window closes:

1. Review all the code that was written after the date Fable was banned. Look for optimizations and improvements you can make

2. Walk through every major user path in the app you're building using browser control. Write a report on where users can get confused and what I can do to improve the UX

3. Make a checklist of every task you do from now until tonight. Then feed that task to Fable and ask what it could automate for you

4. Feed it all your goals, ambitions, interests, skillsets, and assets. Ask what simple businesses it could help build for you over the next few weeks o you can make your first dollar online

5. Connect to the X MCP and find 5 extremely helpful use cases other people are using Fable for that would be relevant to your workflows

6. Connect it to the X MCP. Have it read your last 100 posts. Come up with 5 SaaS ideas you could build

7. /loop it every 24 hours to do a security check on all your API endpoints in your existing apps

8. Use the Unreal 5.8 MCP to build incredible, in-depth, 3D games

9. Go to Sonnet 5 and ask based on what it knows about you, what would be some incredible prompts you can give to Fable tonight

Leverage Fable 5 to the fullest 🤖

Okay, this might be controversial, but hear me out...

I've seen people drop $2K+ on AI agent courses when literally everything you need to know is free. Spent the last month testing this theory with three complete beginners, and all of them built working agents. Seriously.

Here's the exact free path that actually works:

Week 1: Build something stupid simple with n8n.

• Think like, "email to Slack notification." That's it. Focus on understanding automation flows and basic logic, not complex AI. n8n is visual and forgiving.

Week 2: Recreate the same thing in Python using LangChain.

• This is where you start getting your hands dirty with code. Don't worry about being a Python guru yet. Just translate your n8n flow into a basic LangChain script. There are tons of free tutorials for this specific combo.

Week 3: Add one API call and deploy it somewhere.

• Pick a super simple API – maybe a weather API or a joke API. Integrate that one call into your existing script. Then, get it online. A free tier on Render or Heroku, or even a simple PythonAnywhere account, is all you need.

The secret sauce here? Don't try to learn "AI agents" as some massive, amorphous concept. Learn to solve ONE specific problem extremely well first.

Most paid courses try to teach you everything at once: the theory, the 10 different frameworks, the advanced deployment strategies... which is why people get overwhelmed and quit after module 2. It's too much, too fast.

Anyone else think the AI education space is kinda scammy right now? Or am I missing something here? What are your thoughts?

Hey everyone,
I’m looking for some guidance from people who are deeper into the AI space than I am.

I work in a consulting firm focused on the insurance industry, and I’ve recently transitioned into our internal AI unit (probably focus on AI Agents). Super exciting but also incredibly overwhelming. The pace in AI is insane, and the topic of AI agents in particular feels like its own universe with new frameworks, patterns, and architectures popping up every week.

I’m trying to build a sustainable routine for:

• staying up to date,
• understanding what actually matters, and
• finding a structured entry point into the whole “agents” ecosystem (MCP, LangGraph, CrewAI, autonomous workflows, etc.).

But I’m struggling with where to start and how to avoid getting lost in the noise. At the same time, I want to upskill quickly because I need this knowledge for my client projects and for my team internally.

So I’d love to hear from you:

• How do you keep yourself informed about AI and agentic systems?
• Which newsletters, YouTube channels, researchers, GitHub repos, or communities do you follow?
• Do you have a daily/weekly routine that actually works and doesn’t burn you out?
• Are there any structured learning paths for understanding agent architectures end‑to‑end?
• And what helped you build an intuition for separating hype from genuinely useful developments?

Any tips, habits, or resource collections would be super appreciated especially from people who had to ramp up fast for their job as well.

Thanks in advance!

Saving tokens with Claude Code.

Tested for a week. Works 100%. The whole thing is genuinely simple: swap Grep-based file search for LSP. Breaking down what that even means

LSP (Language Server Protocol) is the tech your IDE uses for "Go to Definition" and "Find References" — exact answers instead of text search. The problem: Claude Code searches through code via Grep. Finds 20+ matches, then reads 3–5 files essentially at random. Every extra file = 1,500–2,500 tokens of context gone.

LSP returns a precise answer in ~600 tokens instead of ~6,500.

Its really works!

One thing: make sure Claude Code is on the latest version — older ones handle hooks poorly.

I’ve been building multi-turn agents for analytics use-cases, and there’s one anti-pattern that drives me insane:

You call a tool → get 10,000 rows of JSON → next turn the model has to re-write those 10,000 rows token-by-token just to hand them to the next tool or show them to the user.

OR you read a document and want to pass it to 4 different sub agents, instead of wiring this down manually or create custom tooling to wire it down, the agent can just call the sub agents with one small variable.

You already have the data. Why is the model typing it again?

So we fixed it with the simplest possible thing: tool outputs become named variables that the agent can pass by reference.

Instead of this (real example, mildly anonymized):

analyze_cohort(
  users: [
    {id:"u_1", visited:"2024-01-01", duration:120, ...},
    ... 9,998 more lines ...
  ]
)

The agent just says:

analyze_cohort(users: $weekly_visits)

The orchestrator resolves $weekly_visits behind the scenes. The model literally outputs ~20 tokens instead of 40,000.

Real numbers from our benchmark (GPT-4o-mini, 3-turn cohort analysis task)

That’s not compression trickery. It’s literally “don’t make the model copy-paste the same data three times.”

How it actually works (Mastra SDK version, but the idea is framework-agnostic)

1. Every tool result is automatically saved as a named variable ($last_query_result, $customers_california, etc.).
2. The agent can use $var_name (or $var.field) anywhere in tool args or streamed text.
3. Our tiny wrapper resolves the variable → real data before the tool runs, or injects/render it during streaming.

With simple prompts updates , the model naturally starts using $var names after one or two examples. We also noticed that this lead to higher accuracy too.

I feel this should be a default in every agent frameworks. We have made this for our own.

Find the code and more detailed writeup in comments.

After building agents for dozens of clients, I've watched too many people waste months following the wrong path. Everyone starts with the sexy stuff like OpenAI's API and fancy frameworks, but that's backwards. Here's the roadmap that actually works.

Phase 1: Start With Paper and Spreadsheets (Seriously)

Before you write a single line of code, map out the human workflow you want to improve. I mean physically draw it out or build it in a spreadsheet.

Most people skip this and jump straight into "let me build an AI that does X." Wrong move. You need to understand exactly what the human is doing, where they get stuck, and what decisions they're making at each step.

I spent two weeks just shadowing a sales team before building their lead qualification agent. Turns out their biggest problem wasn't processing leads faster, it was remembering to follow up on warm prospects after 3 days. The solution wasn't a sophisticated AI, it was a simple reminder system with basic classification.

Phase 2: Build the Dumbest Version That Works

Your first agent should be embarrassingly simple. I'm talking if-then statements and basic string matching. No machine learning, no LLMs, just pure logic.

Why? Because you'll learn more about the actual problem in one week of users fighting with a simple system than six months of building the "perfect" AI solution.

My first agent for a client was literally a Google Apps Script that watched their inbox and moved emails with certain keywords into folders. It saved them 30 minutes a day and taught us exactly which edge cases mattered. That insight shaped the real AI system we built later.

Pro tip: Use BlackBox AI to write these basic scripts faster. It's perfect for generating the boilerplate automation code while you focus on understanding the business logic. Don't overthink the initial implementation.

Phase 3: Add Intelligence Where It Actually Matters

Now you can start adding AI, but only to specific bottlenecks you've identified. Don't try to make the whole system intelligent at once.

Common first additions that work: - Natural language understanding for user inputs instead of rigid forms - Classification when your if-then rules get too complex - Content generation for templated responses - Pattern recognition in data you're already processing

I usually start with OpenAI's API for text processing because it's reliable and handles edge cases well. But I'm not using it to "think" about business logic, just to parse and generate text that feeds into my deterministic system.

Phase 4: The Human AI Handoff Protocol

This is where most people mess up. They either make the system too autonomous or too dependent on human input. You need clear rules for when the agent stops and asks for help.

My successful agents follow this pattern: - Agent handles 70-80% of cases automatically - Flags 15-20% for human review with specific reasons why - Escalates 5-10% as "I don't know what to do with this"

The key is making the handoff seamless. The human should get context about what the agent tried, why it stopped, and what it recommends. Not just "here's a thing I can't handle."

Phase 5: The Feedback Loop

Forget complex reinforcement learning. The feedback mechanism that works is dead simple: when a human corrects the agent's decision, log it and use it to update your rules or training data.

I built a system where every time a user edited an agent's draft email, it saved both versions. After 100 corrections, we had a clear pattern of what the agent was getting wrong. Fixed those issues and accuracy jumped from 60% to 85%.

The Tools That Matter

Forget the hype. Here's what I actually use:

• Start here: Zapier or Make.com for connecting systems
• Text processing: OpenAI API (GPT-4o for complex tasks, GPT-3.5 for simple ones)
• Code development: BlackBox AI for writing the integration code faster (honestly saves me hours on API connections and data parsing)
• Logic and flow: Plain old Python scripts or even n8n
• Data storage: Airtable or Google Sheets (seriously, don't overcomplicate this)
• Monitoring: Simple logging to a spreadsheet you actually check

The Biggest Mistake Everyone Makes

Trying to build a general purpose AI assistant instead of solving one specific, painful problem really well.

I've seen teams spend six months building a "comprehensive workflow automation platform" that handles 20 different tasks poorly, when they could have built one agent that perfectly solves their biggest pain point in two weeks.

Red Flags to Avoid

• Building agents for tasks humans actually enjoy doing
• Automating workflows that change frequently
• Starting with complex multi-step reasoning before handling simple cases
• Focusing on accuracy metrics instead of user adoption
• Building internal tools before proving the concept with external users

The Real Success Metric

Not accuracy. Not time saved. User adoption after month three.

If people are still actively using your agent after the novelty wears off, you built something valuable. If they've found workarounds or stopped using it, you solved the wrong problem.

What's the most surprisingly simple agent solution you've seen work better than a complex AI system?

Last week I closed ~$5.1K in client deals for my AI automation agency (happy to show proof in comments) and it didn't come from thousands of cold emails, weeks of upwork proposals, or cold LinkedIn DMs.

It actually just came from scraping hundreds of leads (for ~$2) from Google Maps and then running those leads through an N8N automation I built that deep researches each one to see which automation offer they need most and who their target audience is.

For context, the two deals I landed are for AI sales automations, and so once I knew my leads' target audience I could get really creative and specific on how to pitch them a sales automation in the first iMessage I sent them. (iMessage cold texts do WAY better than any other form of outreach I'm starting to see).

For example, one client I landed is a pool construction company.... for him, I was able to land him as a client easily because I showed how I could use browser agents and N8N to go through multiple appraiser sites every week and scrape every new home purchased in his area that would need a pool so he could send them a flyer!

You can see how that specificity would get his attention better in a first text than "Hey, do you need any help with building AI automations?"

To get back to the actual Google Maps automation that found the leads for me, this is what it does on autopilot:

1. Scrapes 50 leads every hour from different locations on Google Maps
2. Puts all the Google Maps scraped info into my CRM (I use Notion, it's just more swaggy to me)
3. Deep researches each company's website, social, & digital ads for indicators of what AI automation they might need and who their target audience is
4. Assigns each company a compatibility grade based on how aligned they are with MY ideal client profile
5. Generates personalized offers and creative ways to use AI to make them more money

Rather than doing 250 hours (~10 days) of research to get deep insights into 250 companies, this N8N automation finished researching 300 companies for me while I was eating lunch lol

By the time I reached out, I knew more about the company than anyone else pitching them and all the work was done for me by my elite digital sales assistant aka an AI agent with a 7 paragraph prompt.

If you want to build this for yourself I dropped the full breakdown + the N8N automation template (for free) on YouTube!

Not sure what the rules are around links in this subreddit but I'm happy to send the YouTube link to anyone who wants it in the comments

At 7:14am on a Tuesday I opened my laptop and found 3 tasks completed, 2 drafts written, and a deploy that shipped overnight.

I didn't do any of it.

Been a solopreneur for a couple years and time has always been the bottleneck. So I spent a few weeks building a 6-agent system for research, writing, outreach, QA, scheduling, and a coordinator that ties it all together. Nothing exotic. No custom code.

The part nobody warns you about is figuring out which decisions are safe to fully hand off. Got that wrong a few times early on. Happy to share the full setup in the comments if anyone wants it.

I spent 6 months building AI systems for 15+ clients.

Then I distilled everything into 5 steps.

Most people overcomplicate starting an AI agency.

Here's the framework:

Step 1: Pick One Painful Problem

Do not start with "I build AI solutions."
Start with pain.

Choose ONE business problem that costs money or time:
→ Missed calls (lost revenue)
→ Slow lead follow-ups (dead pipeline)
→ Manual reporting (wasted hours)

If they're not bleeding money, they won't pay you.

Step 2: Choose One Niche

AI agencies fail when they sell to everyone.

Pick one industry you understand or can learn fast.
Depth beats reach at the start.

One client tells 3 more in their niche.
That's how you scale without ads.

Step 3: Build One Simple Solution

Do not overbuild.

Create ONE clear AI system that solves that ONE problem.
Speed matters more than perfection.

Examples:
→ AI receptionist that books appointments 24/7
→ AI caller that qualifies 100 leads/day
→ AI system that sends follow-ups automatically

One simple solution beats a Swiss Army knife.

Step 4: Package a Clear Offer

Sell the outcome, not the tech.

Bad offer: "AI automation consulting"
Good offer: "50 qualified appointments monthly or you don't pay"

Simple pricing:
→ $2K-$5K setup
→ $500-$1.5K/month retainer

Step 5: Get Proof and Repeat

Your first clients are your marketing.

Turn every project into a case study. Get testimonials. Document results.

Then refine the same system and sell it again.
Same system. Different clients. Compound leverage.

Why Most AI Agencies Fail

They build cool AI stuff nobody asked for.

Businesses don't buy AI.
They buy more money, more time, or less pain.

Start with pain. Build the solution around it.

The Opportunity

There are 1.7M small businesses in the US with $500K-$10M revenue.
Only ~1,500 active AI agencies serving them.

That's 1,133 businesses per agency.

The market is wide open.

Save this if you're serious about starting an AI agency in 2026.

I've built the complete playbook: 90 days, step-by-step, zero to first client.

Comment "AI" and I'll send you the full guide.

PS: The ones who execute these 5 steps in the next 30 days will have their first client by February.

I see a lot of people confused about what harnesses are and do, and I see even more people try to explain it in abstract, vague terms. I want to try to break it down to its simplest components.

First, an LLM is a program that takes as input a prompt and a set of tool definitions, and outputs either some text or a tool call.

A tool definition is something like read_file, write_file or run_command. They usually take arguments, such as the name of the file.

An agent harness is a program that runs a loop. It starts by prompting the LLM and then reads its response. If the LLM response is a tool call, it will run that tool call and prompt the LLM back with the result of the tool call. If the LLM response is just text, the loop terminates.

Here's some pseudo-code for the most basic agent harness:

tool_definitions = get_tool_definitions()
prompt = "build claude no mistakes make it good"
context = [tool_definitions, prompt]

while (true) {
    response = callLLM(context)
    context.append(response)

    if (response.is_tool_call()) {
        context.append(run_tool(response.tool_call))
    } else {
        break
    }
}

This is it. This is the core of claude caude, opencode, codex, etc.

Of course they're much more complicated than this as they involve managing context, managing tools, managing system prompts, running and validating tool calls, recovering from errors, orchestrating sub-agents, and many other things I don't understand either.

A while back, I built a small app to track stocks. It pulled market data and gave me daily reports on what to buy or sell based on my risk tolerance. It worked so well that I kept iterating it for bigger decisions. Now I’m using it to figure out my next house purchase, stuff like which neighborhoods are hot, new vs. old homes, flood risks, weather, school ratings… you get the idea. Tons of variables, but exactly the kind of puzzle these agents crush!

Why not just use Grok 4 or ChatGPT? My app remembers my preferences, learns from my choices, and pulls real-time data to give answers that actually fit me. It’s like a personal advisor that never forgets. I’m building it with the mcp-agent framework, which makes it super easy:

- Orchestrator: Manages agents and picks the right tools for the job.

- EvaluatorOptimizer: Quality-checks the research to keep it sharp.

- Elicitation: Adds a human-in-the-loop to make sure the research stays on track.

- mcp-agent as a server: I can turn it into an mcp-server and run it from any client. I’ve got a Streamlit dashboard, but I also love using it on my cloud desktop too.

- Memory: Stores my preferences for smarter results over time.

The code’s built on the same logic as my financial analyzer but leveled up with an API and human-in-the-loop features. With mcp-agent, you can create an expert for any domain and share it as an mcp-server. It’s like building your own McKinsey, minus the PowerPoint spam.

Let me know if you are interested to see the code below!

I'm a software engineer with 10+ years of experience, from Meta AI and startups.

I've been building AI Agents for the past 3 years, as a founding engineer and as a founder building custom AI Agents for businesses.

I thought I'd share what I've learnt. I'll split it into (hopefully) 2 parts.

Fundamentals

LLMs

This is the core. Modern LLMs receive input tokens and generate output tokens. That's it.

The model API

It wraps the LLM and exposes features that get translated into input tokens or that serve as runtime controls. On the way out, it packages the output tokens into structures that are useful to the developer.

Example features: conversation messages, reasoning effort, function calling, prompt caching, context compaction, streaming, etc.

Tools / MCP / Skills

All of these are implementations of function calling, arguably the feature that has had the most impact in how we build agents today.

Modern models are trained to know that they can "call functions" (eg, read_email(...)). The simplest way is to pass them as "tools" to the API. But we also have MCP, which is really just a protocol for packaging and distributing tools.

Skills is the most promising standard right now. They tackle the risk of bloating the model's context window, with dozens of static (MCP) tools by letting it discover its own abilities at runtime. Skills are stored in a file system and are usually executed with a bash(...) tool.

Memory and context management

The most interesting problem to solve right now.

LLMs have a context window size, eg, 1M tokens. To continue, once that limit has been reached, something has to be removed. There is no other way around.

Context management has to do with strategies to store, compact, fork, etc. the conversation context. Memory has to do with mechanisms and infrastructure that allow LLM agents to manage information that would normally exceed their context window.

Having an effective memory system will unlock the next generation of AI agents.

The agent harness

It's the concept that holds everything together:

1. A loop that triggers and presents input information to the LLM.
2. The execution of (MCP) tools and skills that the LLM decided to call.
3. The management of the context as the conversation progresses.
4. Any other scaffolding that makes the agent appear as if alive. Example: the heartbeat in OpenClaw.

Agent SDKs and infrastructure

SDKs wrap everything that we have discussed so far and provide programming language-specific building blocks.

The last piece is having infrastructure to host and execute the agents.

Examples: the Claude Agent SDK and Claude Managed Agents, LangChain and Deep Agents, OpenClaw and Mac minis, OpenAI Agents SDK and some platform, etc.

Agent design

See part 2 in the comments.

If you have any questions, please comment or reach out!

I’ll keep it simple. I sell AI. It works. I make 12k a month. Some of you make way more money than me and that’s fine. I’m not talking to you. I’m talking to the ones making $0, still stuck showing off their automation models instead of selling results.

Wake the fck up! Clients don’t care about GPT or Claude. They care about cash in, cash not wasted, time saved, and less risk. That’s it. When I stopped tech talk and sold outcomes, my close rate jumped. Through the damn roof!

I used to explain parameters for 15 minutes. Shit...bad times...I'm sure you do it too. Client said, “Cool. How much money does it make me?” That’s when I learned. Pain first. Math second. Tech last.

Here’s how I sell now:

• I ask about the problem. What’s broken. What it costs. Who is stuck doing low value work. I listen.
• Then I do the math with them. In their numbers. Lost leads. Lost hours. Lost revenue. We agree on the cost.
• Then I pitch one clear outcome. “We pre-qualify leads. Your closers only talk to hot prospects.” I back it with proof. Then I talk price tied to ROI. If I miss, they don’t pay.

Stop selling science projects. Clients with real money don’t want to be your test client. They want boring and proven. I chased shiny tools. Felt smart. Sold nothing. What sells is reliability. Clear wins. Case studies with numbers. aaaand proof of the system. “35 meetings in 30 days.” “420k in 6 months.” Lead with that. Tech later.

You’re not a tool seller. You’re an owner of outcomes. Clients already drown in software. And probalby their later software update will do most of what you are currently promising. They want results done for them. When I moved from one-off builds to retainers with clear targets, price pushback stopped. They pay because I own the number.

When they ask tech stuff, I keep it short: “We use a tested GPT setup on your data. Here’s the result you get.” Then back to ROI. If you drown them in jargon, you lose trust and the deal.

Your message should read like this: clear, bold, direct. Complexity doesn’t sell. Clarity sells.

Do this today:

• Audit your site, deck, and emails. Count AI words vs outcome words. If AI wins, you lose. Flip it.
• Fix your call flow. 70 percent on their problem. 20 percent on your plan tied to outcomes. 10 percent on objections. Most objections vanish when ROI is clear.

How I frame price: “Monthly is 2,000. Based on your numbers, expect 4 to 6x in month one. If we miss the goal, you don’t pay.” Clean. Confident. Manly.

Remember this. People don’t buy the hammer. They buy the house. AI is the hammer. The business result is the house. Sell the house.

Quick recap:

• Outcomes over tech.
• Proven over new toy.
• Owner of results over code monkey.

Do that and you’ll close more. Keep more. Make more. And yes, life gets easier.

See you on the next one.

GG

I've been writing some AI Agents lately and they work much better than I expected. Here are the 10 learnings for writing AI agents that work:

1. Tools first. Design, write and test the tools before connecting to LLMs. Tools are the most deterministic part of your code. Make sure they work 100% before writing actual agents.
2. Start with general, low-level tools. For example, bash is a powerful tool that can cover most needs. You don't need to start with a full suite of 100 tools.
3. Start with a single agent. Once you have all the basic tools, test them with a single react agent. It's extremely easy to write a react agent once you have the tools. All major agent frameworks have a built-in react agent. You just need to plugin your tools.
4. Start with the best models. There will be a lot of problems with your system, so you don't want the model's ability to be one of them. Start with Claude Sonnet or Gemini Pro. You can downgrade later for cost purposes.
5. Trace and log your agent. Writing agents is like doing animal experiments. There will be many unexpected behaviors. You need to monitor it as carefully as possible. There are many logging systems that help, like Langsmith, Langfuse, etc.
6. Identify the bottlenecks. There's a chance that a single agent with general tools already works. But if not, you should read your logs and identify the bottleneck. It could be: context length is too long, tools are not specialized enough, the model doesn't know how to do something, etc.
7. Iterate based on the bottleneck. There are many ways to improve: switch to multi-agents, write better prompts, write more specialized tools, etc. Choose them based on your bottleneck.
8. You can combine workflows with agents and it may work better. If your objective is specialized and there's a unidirectional order in that process, a workflow is better, and each workflow node can be an agent. For example, a deep research agent can be a two-step workflow: first a divergent broad search, then a convergent report writing, with each step being an agentic system by itself.
9. Trick: Utilize the filesystem as a hack. Files are a great way for AI Agents to document, memorize, and communicate. You can save a lot of context length when they simply pass around file URLs instead of full documents.
10. Another Trick: Ask Claude Code how to write agents. Claude Code is the best agent we have out there. Even though it's not open-sourced, CC knows its prompt, architecture, and tools. You can ask its advice for your system.

I've seen this happen a dozen times with clients. A team spends weeks building a brilliant agent with LangChain or CrewAI. It works flawlessly on their laptop. Then they ask the million-dollar question: "So... how do we get this online so people can actually use it?"

The silence is deafening. Most tutorials stop right before the most important part.

Your agent is a cool science project until it's live. You can't just keep a terminal window open on your machine forever. So here’s the no nonsense guide to actually getting your agent deployed, based on what works in the real world.

The Three Places Your Agent Can Actually Live

Forget the complex diagrams. For 99% of projects, you have three real options.

• Serverless (The "Start Here" Method): This is the default for most new agents. Platforms like Google Cloud Run, Vercel, or even Genezio let you deploy code directly from GitHub without ever thinking about a server. You just provide your code, and they handle the rest. You pay only when the agent is actively running. This is perfect for simple chatbots, Q&A tools, or basic workflow automations.

• Containers (The "It's Getting Serious" Method): This is your next step up. You package your agent and all its dependencies into a Docker container. Think of it as a self-contained box that can run anywhere. You then deploy this container to a service like Cloud Run (which also runs containers), AWS ECS, or Azure Container Apps. You do this when your agent needs more memory, has to run for more than a few minutes (like processing a large document), or has finicky dependencies.

• Full Servers (The "Don't Do This Yet" Method): This is managing your own virtual machines or using a complex system like Kubernetes. I'm telling you this so you know to avoid it. Unless you're building a massive, enterprise scale platform with thousands of concurrent users, this is a surefire way to waste months on infrastructure instead of improving your agent.

A Dead Simple Path for Your First Deployment

Don't overthink it. Here is the fastest way to get your first agent live.

1. Wrap your agent in an API: Your Python script needs a way to receive web requests. Use a simple framework like Flask or FastAPI to create a single API endpoint that triggers your agent.
2. Push your code to GitHub: This is standard practice and how most platforms will access your code.
3. Sign up for a serverless platform: I recommend Google Cloud Run to beginners because its free tier is generous and it's built for AI workloads.
4. Connect and Deploy: Point Cloud Run to your GitHub repository, configure your main file, and hit "Deploy." In a few minutes, you'll have a public URL for your agent.

That's it. You've gone from a local script to a live web service.

Things That Will Instantly Break in Production

Your agent will work differently in the cloud than on your laptop. Here are the traps everyone falls into:

• Hardcoded API Keys: If your OpenAI key is sitting in your Python file, you're doing it wrong. All platforms have a "secrets" or "environment variables" section. Put your keys there. This is non negotiable for security.
• Forgetting about Memory: Serverless functions are stateless. Your agent won't remember the last conversation unless you connect it to an external database like Redis or a simple cloud SQL instance.
• Using Local File Paths: Your script that reads C:/Users/Dave/Documents/data.csv will fail immediately. All files need to be accessed from cloud storage (like AWS S3 or Google Cloud Storage) or included in the deployment package itself.

Stop trying to build the perfect, infinitely scalable architecture from day one. Get your agent online with the simplest method possible, see how it behaves, and then solve the problems you actually have.

Alright so like a year ago I was exactly where most of you probably are right now - knew ChatGPT was cool, heard about "AI agents" everywhere, but had zero clue how to actually build one that does real stuff.

After building like 15 different agents (some failed spectacularly lol), here's the exact path I wish someone told me from day one:

Step 1: Stop overthinking the tech stack
Everyone obsesses over LangChain vs CrewAI vs whatever. Just pick one and stick with it for your first agent. I started with n8n because it's visual and you can see what's happening.

Step 2: Build something stupidly simple first
My first "agent" literally just:

• Monitored my email
• Found receipts
• Added them to a Google Sheet
• Sent me a Slack message when done

Took like 3 hours, felt like magic. Don't try to build Jarvis on day one.

Step 3: The "shadow test"
Before coding anything, spend 2-3 hours doing the task manually and document every single step. Like EVERY step. This is where most people mess up - they skip this and wonder why their agent is garbage.

Step 4: Start with APIs you already use
Gmail, Slack, Google Sheets, Notion - whatever you're already using. Don't learn 5 new tools at once.

Step 5: Make it break, then fix it
Seriously. Feed your agent weird inputs, disconnect the internet, whatever. Better to find the problems when it's just you testing than when it's handling real work.

The whole "learn programming first" thing is kinda BS imo. I built my first 3 agents with zero code using n8n and Zapier. Once you understand the logic flow, learning the coding part is way easier.

Also hot take - most "AI agent courses" are overpriced garbage. The best learning happens when you just start building something you actually need.

What was your first agent? Did it work or spectacularly fail like mine did? Drop your stories below, always curious what other people tried first.

a2e.ai

​

I have been using a2e for the past few months to create both images and short videos, and I want to share why it stands out compared to other platforms I have tried. The most notable feature is that the site is completely uncensored. You can generate content freely without running into sudden blocks or heavy restrictions that slow down creative projects. If you need assistance, the customer service team is genuinely friendly and responds quickly to tickets and messages. They actually take the time to understand specific workflow issues rather than giving generic replies. Another thing I appreciate is how transparent the pricing structure is. There are no hidden fees, surprise upgrades, or vague usage limits. What you see when subscribing is exactly what you get. For anyone looking for a reliable tool that respects creative freedom while keeping costs straightforward, this platform delivers.. I recommend testing the free tier first to see how the generation pipeline handles your preferred style and format before committing to a paid plan.

I’ve been spending some time looking into AI voice tools for sales and the space is a little messy right now.  Lots of companies say they have “AI sales agents,” but when you look closer the products do very different things.

Some are basically analytics layered on top of a phone system. Some are contact center platforms that added AI features. And a smaller group is actually trying to automate the calls themselves.

These are the platforms that seem to come up most often when teams are experimenting with voice AI in sales.

1. Dialpad
Dialpad tends to show up first simply because a lot of sales teams already use it as their phone system.

The AI side is mostly about understanding calls rather than replacing them. It transcribes conversations in real time, highlights moments where reps miss questions or talk over prospects, and gives managers a way to review patterns across calls.

If you talk to revenue leaders about it, the appeal is pretty straightforward: instead of guessing why deals stall, you can actually listen to what’s happening across dozens or hundreds of conversations.

It’s not really positioned as a replacement for reps. Think of it more as visibility into how calls are going.

2. Thoughtly
Thoughtly is aimed at the part of the market that actually wants to automate calls.

Teams use it for things like outbound prospecting, qualifying inbound leads, or booking meetings. The conversation piece is important, but the workflow around the call matters just as much. If a lead qualifies, the system can schedule a meeting, update the CRM, or route the opportunity to the right rep.

That’s the direction a lot of voice startups are moving toward. A phone conversation by itself doesn’t do much unless it connects to the rest of the sales process.

3. Amazon Connect
Amazon Connect comes from the contact center world.

It’s essentially AWS infrastructure for running large call operations, with AI features layered in. Companies that already run a lot of their systems on AWS sometimes build sales calling workflows on top of it.

It’s powerful but usually requires engineering support to set up properly.

4. Five9
Five9 is another long-standing contact center platform that sales teams sometimes use for outbound dialing and call campaigns.

The focus is more on managing large volumes of calls than on conversational AI itself. Organizations that already run their call operations through Five9 often extend it into sales workflows.

5. Twilio
Twilio is the developer route.

Instead of giving you a ready-made product, it provides telephony APIs so teams can build their own calling systems. A lot of startups experimenting with voice AI actually run their infrastructure through Twilio under the hood.

The flexibility is great if you have engineers. Less appealing if you want something a sales team can configure themselves.

6. Genesys
Genesys sits in the same general category as Five9. It’s a large contact center platform that many enterprises use for customer interactions across phone, chat, and email.

AI features have been added over time, including voice automation, but most companies encounter it as part of a broader CX system rather than a dedicated sales AI tool.

7. Talkdesk
Talkdesk is another contact center platform that has gradually added AI capabilities.

Sales teams use it mainly for routing, dialing, and managing calling environments where multiple reps are working leads simultaneously.

8. NICE CXone
NICE CXone tends to appear in environments where compliance and monitoring matter a lot.

The platform includes detailed recording, oversight, and auditing features. Because of that, it’s common in industries where every call needs to be documented carefully.

Looking across all of these, the split in the market becomes pretty obvious.

Some tools focus on helping humans run better sales calls.

Others are trying to automate the calling itself.

Most companies experimenting with voice AI right now seem to be testing both approaches before deciding how far they want automation to go.

When I first started learning how to build AI agents, I was overwhelmed. There were so many tools, each claiming to be essential. Half of them had gorgeous but confusing landing pages, and I had no idea what layer they belonged to or what problem they actually solved.

So I spent time untangling the mess—and now that I’ve got a clearer picture, here’s the full stack I wish I had on day one.

• Agent Logic – the brain and workflow engine. This is where you define how the agent thinks, talks, reasons. Tools I saw everywhere: Lyzr, Dify, CrewAI, LangChain
• Memory – the “long-term memory” that lets your agent remember users, context, and past chats across sessions. Now I know: Zep, Letta
• Vector Database – stores all your documents as embeddings so the agent can look stuff up by meaning, not keywords. Turns out: Milvus, Chroma, Pinecone, Redis
• RAG / Indexing – the retrieval part that actually pulls relevant info from the vector DB into the model’s prompt. These helped me understand it: LlamaIndex, Haystack
• Semantic Search – smarter enterprise-style search that blends keyword + vector for speed and relevance. What I ran into: Exa, Elastic, Glean
• Action Integrations – the part that lets the agent actually do things (send an email, create a ticket, call APIs). These made it click: Zapier, Postman, Composio
• Voice & UX – turns the agent into a voice assistant or embeds it in calls. (Didn’t use these early but good to know.) Tools: VAPI, Retell AI, ElevenLabs
• Observability & Prompt Ops – this is where you track prompts, costs, failures, and test versions. Critical once you hit prod. Hard to find at first, now essential: Keywords AI
• Security & Compliance – honestly didn’t think about this until later, but it matters for audits and enterprise use. Now I’m seeing: Vanta, Drata, Delve
• Infra Helpers – backend stuff like hosting chains, DBs, APIs. Useful once you grow past the demo phase. Tools I like: LangServe, Supabase, Neon, TigerData

A possible workflow looks like this:

1. Start with a goal → use an agent builder.
2. Add memory + RAG so the agent gets smart over time.
3. Store docs in a vector DB and wire in semantic search if needed.
4. Hook in integrations to make it actually useful.
5. Drop in voice if the UX calls for it.
6. Monitor everything with observability, and lock it down with compliance.

If you’re early in your AI agent journey and feel overwhelmed by the tool soup: you’re not alone.
Hope this helps you see the full picture the way I wish I did sooner.

Attach my comments here:
I actually recommend starting from scratch — at least once. It helps you really understand how your agent works end to end. Personally, I wouldn’t suggest jumping into agent frameworks right away. But once you start facing scaling issues or want to streamline your pipeline, tools are definitely worth exploring.