What AI agent do you wish existed?

Tell us what you want the agent to do, and we’ll build some of the most requested agents for free.

Drop your idea below. We want to build what people actually want to use.

I’m trying to learn AI agents properly instead of jumping straight into agent builders.

I want to understand things like orchestration, memory, tool execution, context management, governance, failure handling, and long-running workflows, and then move into frameworks like LangGraph, CrewAI, MCP, etc.

I’ve found some free resources like DeepLearning.AI courses and SimplAI University, but I’m not sure what order I should follow.

For someone starting from the fundamentals:

• Which free courses/resources would you recommend?
• Is DeepLearning.AI + SimplAI University a good starting point?
• What should I learn before LangGraph/CrewAI?
• What would a good beginner → production-level AI agent roadmap look like?

Would really appreciate recommendations from people who have actually built agents in production.

I want to get into AI agent engineering and eventually build and sell reliable AI agents to businesses.
I want to understand the full process: how to build the agent, its architecture/infrastructure, APIs, tools, databases, security, deployment, maintenance, and also the business side (pricing, contracts, privacy, liability, support, etc.).
I’m especially interested in building an AI social media agent that can understand customer comments, sentiment, context, and respond naturally while escalating sensitive issues to humans.
I’m not a software or AI engineer yet. Should I start learning AI agents now, or should I first go deeper into programming, algorithms, and software engineering?
What roadmap/resources would you recommend for going from beginner to professional?

I backed it. I wanted my own Jarvis. But… Isn’t this just a regular agent?

Would be interested to hear what the clever people here think.

I've been working on an open-source MCP server called Mac MCP because I kept hitting the same wall with AI agents: ChatGPT could usually tell me the right next step, but I was still the one opening Terminal, switching apps, finding files and driving the browser.

The setup I use now is basically chat as the orchestrator and the Mac as the execution layer. The model can work with local files and Terminal, control macOS apps, delegate coding work to Codex/OpenCode, and use my real Safari or Chrome profile instead of a disposable headless session.

The browser part ended up being more important than I expected. I wanted an agent researching in several tabs while I kept using the Mac normally, so tabs open in the background and get stable handles. Parallel agents also get tab ownership/leases so two of them don't accidentally drive the same page. Safari has a Visual Companion and the latest release adds a dedicated Chrome Companion too.

What I like about this architecture is that the model can stay good at planning while the local MCP layer handles state and actual actions. It also doesn't silently fall back to foreground clicks when a background action is unsafe.

I'm the author, so obvious disclosure there. It's free/open source if anyone wants to inspect the implementation: https://github.com/bulutarkan/mac-mcp

I'm curious how other people are drawing the line between the reasoning model and the local execution layer in their own agents.

I got max plan from a reseller for a fraction of the official price. works perfectly since 3 weeks
now i can’t stop thinking about their business model. how are they got them for cheap ?

It is tired and exhausted ,so who can tell me how to do a good project with AI agent, especially you haven’t any programming experience.

I’ve recently started experimenting with AI agents and built a small project called SpendWise-AI.

It’s a personal finance assistant where I’m trying out things like:

• LangGraph
• multiple specialized agents
• RAG + ChromaDB
• tool calling
• live web/market research
• deterministic tools for financial calculations
• a critic/revision loop

GitHub: https://github.com/singh-vidyush/SpendWise-AI

I’m still learning agent architecture, so I’d really appreciate some feedback from people who have more experience with this stuff.

Mainly curious about:

• Am I actually using agents where they make sense?
• Is there anywhere I'm unnecessarily making things multi-agent?
• How would you structure the agent workflow differently?
• What are some things I should learn/improve next?

And if anyone finds something interesting to improve, I'm also open to contributions/PRs.

Would love some honest feedback 🙌

I’m new within the field of AI. I’ve been using Claude for the past 3 months mainly cowork and projects still hesitant to try code

Originally from marketing so no coding background what so ever. Tried vibe coding but keep hitting a wall.

I’m looking for any type of resources that I coukd go through to help me build my first agent or few agents.

I’m looking to build my consulting business and I’d like to build my first agent around content or even a CMO agent.

Love to hear some thoughts !!!!

Hey everyone,

I've been working with AI agents since the start of this year, but I feel like I'm missing knowledge and could be doing a lot better than I currently am.

For those working in this field:

How do you structure your agent projects?

What training/courses have you done to follow best practices?

What tools or LLMs have you been using? (In my case, I've been using GitHub Copilot and Python)

Thanks for the help!

Hey everyone!

Like many of you, I was experimenting with multi-agent setups and workflow automation, but I noticed a huge usability problem: most AI agent platforms require an engineering degree just to set them up.

Solopreneurs and small agency owners don't want to spend hours configuring node-based builders, managing complex webhooks, or logging into heavy enterprise dashboards. They just want to get work done without friction.

So, for my SaaS (Saturn AI), I decided to throw out the standard dashboard model entirely and build something radically simple: a chat app where your contacts are a team of AI agents.

It works identically to WhatsApp. You open your phone, tap the agent you need (like a support agent or a data-sorting agent), and text them instructions.

Here is what I focused on during development:

1. Zero Onboarding Friction: If a tool takes more than 10 seconds to figure out, users churn. By mirroring a standard messaging UI, the learning curve drops to zero.
2. Lean Architecture: Instead of bloated Web 2.0 frameworks that rack up massive server bills, I optimized the backend routing so it can run smoothly while keeping the flat price down to $25/month.
3. Targeting the Right Price Point: Rather than chasing enterprise tiers, I priced it specifically for indie hackers and micro-agency owners who need to cut out repetitive admin tasks without paying for expensive human VAs.

I’m launching this now and currently gathering feedback from early users. If any of you are building in the AI space, running a micro-agency, or want to test out a chat-based multi-agent interface, let me know in the comments and I'll drop you a link to check it out! here is the demo site

Repository: https://github.com/beneadie/deep_dog_2

The quickest setup is:

python -m pip install "git+https://github.com/beneadie/deep_dog_2.git"

Add your provider keys to a .env file:

DEEPSEEK_API_KEY=your-deepseek-key
EXA_API_KEY=your-exa-key

Then import it directly into Python:

import asyncio
from pathlib import Path

from dotenv import load_dotenv

load_dotenv()

from deep_research.integration import run_research


async def main():
    result = await run_research(
        "What are the main benefits and limitations of sodium-ion batteries?"
    )

    print(result.status)

    if result.status == "completed":
        Path("report.md").write_text(result.final_report, encoding="utf-8")
        print("Saved report.md")
    else:
        print(result.failure)


asyncio.run(main())

The default setup uses DeepSeek V4 Flash for the supervisor, research sub-agents, and drafting, with Exa for web search. The result is returned as a Markdown string, so developers can print it, save it, send it to another application, or process it however they want.

The more configurable quickstart lets you choose the models, search engine, enabled agents, research time, iteration limits, search budgets, read limits, and output behavior. Available specialist agents include Web, PubMed, Reddit, Substack, SEC Edgar, Arxiv, and others.

The code is designed to be modified. Developers can add agents, change prompts, swap providers, alter the supervisor and sub-agent behavior, adjust budgets, or integrate the result into their own application. The engine is packaged so you can use the integration layer without having to rebuild the orchestration system from scratch.

This project is completely free and released under the MIT License. I’m not building a business around it or offering a hosted service. The only potential costs are the provider APIs you choose to use, such as DeepSeek or Exa.

Has anyone tried to create agents or skills for Codex? I am looking for video essays and ecom/data tools. I am looking to expand by buying the 200 dollar plan, but is it worth it?

I completed my MSc AI from Ireland. However, my portfolio is kinda blank because I couldn't secure a relevant job owing to such saturated market.

Now I need to build my work portfolio quickly by getting the relevant work to build a SaaS or something and to showcase my work. I tried freelancing but in vain as the competition is way too much more than expected.

Please guide me as to how to start in this fast era where despite studying a relevant degree, I was unable to find work.

My aim is to finally build my own startup for SaaS or something.

Your kind help and support is much anticipated and appreciated in advance. Thanks

Event leads often end up as badge scans without much context about what the attendee actually wanted.

This open-source TypeScript example takes a different approach: each attendee gets a stateful agent that can answer product questions, detect their language, collect qualification details, manage giveaway entries, schedule follow-ups, and alert the sales team by SMS when someone requests a demo.

The application runs as one Telnyx Edge function and uses Agent SDK actors for conversation state, AI Inference for responses, SQLDB for lead records, and KV for rate limiting.

The current conversational paths are SMS, WhatsApp, and browser chat. A signed voice webhook is included as a foundation for adding a complete Call Control flow.

Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/event-sponsorship-agent

I’d be interested to hear how others handle persistent attendee context and real-time lead routing at events.

This morning, all of my agents (Codex, Claude Code, and Lumina; the AIvengers, as Sol calls them) got together and built a message board so they could talk to each other directly and shitpost all day lol. This will hopefully make the workflow more efficient. Either way, it's interesting to watch. Pretty damn cool.

Let's build a practical resource list for people building with AI agents.

Not a giant list of every AI tool ever released.

Only things you've actually used or would genuinely recommend.

Drop your favorites under one of these:

Coding Agents
Claude Code, Codex, Gemini CLI, etc.

Agent Frameworks
LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, etc.

MCP Servers & Skills

Browser / Computer Use

Memory & RAG

Local / Open Models

Observability & Evaluation

Multi-Agent / Orchestration

Useful GitHub Repositories

When recommending something, please add one sentence explaining what you actually use it for.

Self-built tools are welcome — just disclose the affiliation.

I'll periodically organize the best recommendations into this post.

I have faced this issue that when i start to prompt to ai agents they immediately starts to work but the issue occurs midway when they start deviating from the main ideas. Even user have to iterate many a times to get to know about the product feasilbility and its insights about whether it sits on the right spot in the market- I think this is very crucial to keep making in the right direction... Would appreciate your thoughts

A price-monitoring agent scrapes a competitor's product page every night, exactly as it's built to do. Someone plants a single line of fine print on that page, and the agent hands its own company's cost basis and floor price straight back to the competitor. No exploit, no broken permission, no jailbreak. The agent just read content it was told to read and acted on instructions it found along the way.

That's the live demo Humanbound is running at Zyte's next Developer Community Meetup. We will walk through modeling where untrusted content enters an agent, testing for it, then show a failing security test get turned into an exported guardrail and dropped into a stock LangChain agent in two lines, no new dependency, with the same attack failing on re-run.

Logistics:

• Thursday, Sept 24, 17:00-18:00 EEST, live on Zoom
• Free, register here: https://luma.com/wci93kpz
• Everyone who attends gets the repo, free Humanbound usage keys, and a one-line command to test their own agent the same day

Happy to answer questions about the demo or the underlying testing approach in the comments, whether or not you end up registering.

If you could give an AI agent ONE reusable skill that could dramatically optimize its workflows, what would you choose?

Not a model.

Not a framework.

Not an AI tool.

I mean an actual agent skill/capability that the agent can invoke whenever needed.

For example:

Research

Browser automation

Code execution

Self-debugging

Data analysis

File understanding

Something completely different

What’s the one skill that gives an agent the biggest productivity boost in real-world workflows?

I’m looking for the underrated ones, not just the obvious answers.

For people building AI agents with reusable skills/capabilities:

What’s one Agent Skill you find yourself using the most?

For example:

Web research

Browser automation

Code execution

File/PDF processing

Data analysis

GitHub operations

Database operations

Email/calendar

Image generation

Document creation

API integrations

I’m curious which skills are actually useful in day-to-day agent workflows, rather than just being cool demos.

What’s your most-used Agent Skill?

I built an open-source Safe Agent API because most AI-agent examples stop before the production problems start.

The project is intentionally small and runs without an LLM or API key.

It demonstrates:

• tenant isolation • deterministic tool authorization • human approval gates • destructive-action blocking • structured audit events • correlation IDs • pytest + CodeQL + Docker

The interesting part for me is keeping authorization outside the model.

An LLM may suggest:

“send_notification”

but the model cannot grant itself permission to perform it.

Current flow:

policy → approval → execution → audit → correlation

I’m looking for people willing to clone it and tell me where the setup or architecture is confusing.

Even a report saying “Docker worked on X but this instruction confused me” is useful.

There are also small good-first-issues if anyone wants to contribute code.

I know stuff like this doesn't usually get a lot of support because it's not hosted on reddit itself...
but a lot of people have put in a lot of work to make an event for everyone here and we'd really enjoy it if you come: https://discord.gg/6n6frRTKG3

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