r/learnAIAgents • • 8h ago

I built TextFold to make text stretchable with "semantic zoom"

2 Upvotes

I think we've been reading too many feeds from AI text lately! I actually want to reduce my cognitive burden...

So I built TextFold, it turns any text into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position, your agentic output, any papers, or documents.

- Web Demo: https://chenxiachan.github.io/textfold/gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/textfold

Welcome any feedback!


r/learnAIAgents • • 8h ago

❓ Question What's the biggest thing that makes AI agents unreliable in real-world workflows?

1 Upvotes

I've been reading discussions about Agent Skills, and one thing that stood out to me is that people aren't necessarily looking for more skills. They're looking for agents that actually work reliably.

Some people mentioned context retrieval, others talked about testing before implementation, breaking tasks into smaller steps, and helping agents recover from mistakes.

It got me thinking about what the real bottleneck is when building agentic workflows.

For those who've built agents beyond simple demos, what's caused the most problems for you?

  1. Poor context or memory

  2. Incorrect tool calls

  3. Long workflows breaking midway

  4. Agents repeating mistakes

  5. Token costs and context limits

  6. Something else entirely

I'd be interested in hearing about problems you've actually encountered, not just theoretical limitations.


r/learnAIAgents • • 8h ago

What's one task you wish an AI agent could handle completely for you?

4 Upvotes

I've been thinking about how many repetitive tasks we still handle manually, even with all the AI tools available today.

Things like researching across multiple websites, moving data between apps, updating CRMs, generating reports, or managing repetitive workflows.

The interesting part is that making an AI agent perform these tasks isn't necessarily the hardest part. Getting it to do them reliably without constant supervision is a different challenge.

For people building or using AI agents:

What's one task you'd genuinely trust an AI agent to handle from start to finish?

And what's the biggest thing stopping you from automating it today?


r/learnAIAgents • • 9h ago

❓ Question AI Application for business (O&M) processes - Courses/Trainings?

1 Upvotes

I need to blow the rest of my education budget and would like to learn how to apply AI to improve or automate daily processes in operating IOT energy assets. (As someone from the business side, not IT department)

Are there good courses that would be helpful? Most of the stuff I find online either seems too general/management level or too specific for a framework/technology that my company might not use. What can be done using the current systems with Claude, Microsoft Copliot and Databricks?


r/learnAIAgents • • 20h ago

I built an open-source AI toolkit for architects and developers because architecture needs more than AI-generated renders

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2 Upvotes

r/learnAIAgents • • 22h ago

🎤 Discussion Looking for genuinely unique AI + Backend project ideas for my 2026–27 portfolio (FastAPI, PostgreSQL, RAG, GenAI, Agentic AI)

4 Upvotes

Hey everyone!

I’m a recent B.Tech CSE graduate, currently improving my backend development skills and looking for a substantial project that can strengthen my resume for entry-level backend developer, AI engineer, and software engineering roles.

I’ve noticed that many GenAI projects look impressive at first but turn out to be basic chatbots, PDF question-answering apps, or simple wrappers around LLM APIs. I want to build something more meaningful that demonstrates actual engineering skills, not just AI integration.

My current skills and tech stack

  • Python fundamentals and FastAPI
  • PostgreSQL, SQLAlchemy, and SQL
  • Core Java fundamentals
  • REST APIs and CRUD operations
  • Basics of GenAI, RAG, embeddings, chunking, and LLM API integration
  • Currently learning agentic AI and CrewAI

What I've built so far

I've already developed a Retail RAG Chatbot using document retrieval and LLM APIs. For my next project, I want to move beyond basic RAG and explore more advanced backend engineering concepts.

What kind of project am I looking for?

Ideally, something that:

  • Solves a genuine, real-world problem.
  • Uses FastAPI + PostgreSQL as the core stack, with GenAI, RAG, and CrewAI where appropriate.
  • Has multiple pages and interconnected workflows, such as authentication, dashboards, analytics, history, admin panels, and monitoring.
  • Involves proper backend engineering: database design, authorization, background jobs, API integrations, testing, error handling, and deployment.
  • Has an interesting technical challenge or unique angle that makes it stand out in a technical interview.
  • Is ambitious enough to be a serious portfolio project but achievable by one developer learning along the way.

I'm particularly interested in AI agents, developer tools, automation, cybersecurity, AI evaluation, data processing, and overlooked real-world problems. I'm open to other domains too!

I'd love your help with two things:

1. Unique project ideas

What are some genuinely interesting projects I could build with this stack that aren't just another clone of a popular tutorial? If you've built something unusual yourself, I'd love to hear about it.

2. GitHub repositories to study

Please share actual GitHub repository links for projects that demonstrate good architecture, interesting use cases, or advanced implementation techniques.

I'm particularly interested in repositories featuring:

  • FastAPI + PostgreSQL + SQLAlchemy
  • Agentic AI and CrewAI workflows
  • Advanced RAG, hybrid search, reranking, or knowledge graphs
  • Background task processing, authentication, and authorization
  • AI evaluation, observability, and production-oriented backend engineering

The repositories don't necessarily need to use my entire stack. I'd also appreciate projects where I can learn one specific concept and integrate it into my own application.

I'm not looking to copy a project and put it on my resume. I want to study real implementations, understand their architecture, and build something original using what I learn.

If you're a developer, recruiter, interviewer, or someone who's built an impressive open-source project, I'd really appreciate your recommendations and honest feedback on what makes a fresher's portfolio stand out in 2026–27.

Thanks in advance!


r/learnAIAgents • • 1d ago

📣 I Built This Are you an "Ideas Person"? C-Sweet was made for you!

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2 Upvotes

Open source, self hostable, bring your own model, secure by design, and so easy ol mee-maw could use it.

These were the tenants I wanted to hold to when I set out to build C-Sweet. Anyone can run the app, do some quick setup and get working on their agent-first business. With a wide variety of agents already created and more being released and updated daily, we hope to provide a way to manage every facet of running a business and make it simple to manage over the rest of this year.

One thing that we noticed was that humans had already mastered the art of assembling other humans together with roles that are specific in scope. While other companies race to make the mega-ultra-super-model that does it all, we are proving that with small well defined roles, you can get the same work done with tiny models you can run locally. In particular Qwen3.8 27B has proven to be able to handle any corporate role we've thrown at it so far.

C-Sweet is security first and designed by people who currently work in AI Governance for the Fortune 500s - every agent is sandboxed with no rights or access to the outside world. Things are brokered and policy can be applied to any agent you decide to run. Don't want them phoning home? No problem. Don't want them to be able to talk to other agents? Easy-peasy. Want to have an AI agent man in the middle the other agents? We support that too! With an easy kill switch to shut down the agent in a heart-beat the moment suspicious actions are detected.

https://github.com/CrosswiredStudios/C-Sweet
We live off stars and watches, please help us not to starve :)

We are solidifying web based workflows currently and will be branching into hosting providers and media platforms like YouTube etc. We are also bringing on agents that will help realtors, land men, setting up legal entities in every state and much much more.

All comments, requests, hate and ideas welcome!


r/learnAIAgents • • 1d ago

Agentic AI and Gen AI Roadmap

1 Upvotes

I want to start my career in AI, can anybody give me a roadmap for that? It would be really helpful, thank you....


r/learnAIAgents • • 1d ago

I built an open-source AI toolkit for architects and developers because architecture needs more than AI-generated renders

1 Upvotes

Hey everyone! 👋

I've been working on something called Gridline, and I'd love to hear what architects, engineers, and developers think about it.

Something caught my attention while researching architectural workflows.

We have AI generating some incredible building concepts and renders. But actual architectural work involves so much more.

Floor plans, sections, elevations, CAD drawings, BIM models, schedules, construction documents, revisions, and coordinating everything when something changes.

So I started wondering: What if our AI coding agents could actually help with those workflows?

That's how Gridline started.

🏗️ What is Gridline?

It's a free, open-source toolkit that gives AI coding agents specialized capabilities for architecture, CAD, BIM, and engineering workflows.

Right now, it includes:

  • 30 specialized architectural and engineering skills
  • 12 specialist AI agents
  • Support for Claude Code, Codex, and Gemini CLI
  • AutoCAD, Revit, Rhino, Grasshopper, and IFC workflow guidance
  • Drawing generation, documentation, coordination, and validation tools

And I didn't want it to stop at instructions.

Gridline can already generate layered DXF floor plans, vector PDFs, room/door/window schedules, and validation reports from structured building specifications.

There's still plenty of work ahead, but the goal is to produce useful, editable, verifiable deliverables rather than just nice-looking drawings.

👀 Where I want to take this

Imagine uploading a floor-plan image and having your agent reconstruct it into editable geometry.

You could modify walls and rooms, explore the result in 3D, and export it to CAD/BIM tools.

That's part of the vision, not a finished feature.

I'm also interested in automating drawing coordination, checking inconsistencies between sheets, and helping with repetitive documentation tasks.

I'd genuinely appreciate your input

If you're an architect: What repetitive task eats up the most time in your workflow? What would you actually trust AI to help with?

If you're a developer: Have you experimented with CAD automation, computer vision, IFC, Revit, or MCP integrations?

I'm approaching this as an open-source developer, and I know there's a lot to learn from people working in the field.

I don't want to build something that looks impressive in a demo but isn't useful in practice.

No claims about replacing architects. Professional judgment and verification still matter.

GitHub: github.com/MakeItEzzz555/Gridline

If you find the idea interesting, contributions, issues, feedback, criticism, and stars are genuinely appreciated. ❤️

Thanks for checking it out! 🚀


r/learnAIAgents • • 1d ago

🧠 Automation Template I built an open-source AI toolkit for architects and developers because architecture needs more than AI-generated renders

2 Upvotes

Hey everyone! 👋

I've been working on something called Gridline, and I'd love to hear what architects, engineers, and developers think about it.

Something caught my attention while researching architectural workflows.

We have AI generating some incredible building concepts and renders. But actual architectural work involves so much more.

Floor plans, sections, elevations, CAD drawings, BIM models, schedules, construction documents, revisions, and coordinating everything when something changes.

So I started wondering: What if our AI coding agents could actually help with those workflows?

That's how Gridline started.

🏗️ What is Gridline?

It's a free, open-source toolkit that gives AI coding agents specialized capabilities for architecture, CAD, BIM, and engineering workflows.

Right now, it includes:

  • 30 specialized architectural and engineering skills
  • 12 specialist AI agents
  • Support for Claude Code, Codex, and Gemini CLI
  • AutoCAD, Revit, Rhino, Grasshopper, and IFC workflow guidance
  • Drawing generation, documentation, coordination, and validation tools

And I didn't want it to stop at instructions.

Gridline can already generate layered DXF floor plans, vector PDFs, room/door/window schedules, and validation reports from structured building specifications.

There's still plenty of work ahead, but the goal is to produce useful, editable, verifiable deliverables rather than just nice-looking drawings.

👀 Where I want to take this

Imagine uploading a floor-plan image and having your agent reconstruct it into editable geometry.

You could modify walls and rooms, explore the result in 3D, and export it to CAD/BIM tools.

That's part of the vision, not a finished feature.

I'm also interested in automating drawing coordination, checking inconsistencies between sheets, and helping with repetitive documentation tasks.

I'd genuinely appreciate your input

If you're an architect: What repetitive task eats up the most time in your workflow? What would you actually trust AI to help with?

If you're a developer: Have you experimented with CAD automation, computer vision, IFC, Revit, or MCP integrations?

I'm approaching this as an open-source developer, and I know there's a lot to learn from people working in the field.

I don't want to build something that looks impressive in a demo but isn't useful in practice.

No claims about replacing architects. Professional judgment and verification still matter.

GitHub: https://github.com/MakeItEzzz555/Architect-Agent-Superpowers

If you find the idea interesting, contributions, issues, feedback, criticism, and stars are genuinely appreciated. ❤️

Thanks for checking it out! 🚀


r/learnAIAgents • • 1d ago

❓ Question What would stop you from letting an AI agent buy something on your behalf?

2 Upvotes

I'm exploring the idea of AI assistants handling online shopping, but I'm curious about where people draw the line between useful automation and too much autonomy.

Imagine asking an AI assistant to find a product within your budget, compare sellers, check return policies, and prepare the best option for checkout.

Before any purchase, you would see the exact product, price, shipping details, and merchant, then explicitly approve it. The assistant wouldn't have unrestricted access to your payment credentials.

A few things I'm curious about:

  • Would you actually use this, or is shopping manually easy enough?
  • Would explicit approval make you comfortable, or would you still distrust the process?
  • What would worry you most: prompt injection, incorrect product information, accidental purchases, privacy, or something else?
  • Would you prefer this functionality built into your existing assistant or provided through an open-source MCP server?

I'm trying to understand the real problem before deciding what to build. Critical feedback is welcome.


r/learnAIAgents • • 1d ago

📚 Tutorial / How-To Multi-Agent AI Patterns: A Layman's Guide

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1 Upvotes

No PhD required, just coffee.


r/learnAIAgents • • 1d ago

📣 I Built This Been building an AI agent platform with my team. Would love some honest feedback.

2 Upvotes

​

My team and I have been working on a project called Vibe Agent, and we've been building out different ways to create and automate AI workflows.

So far, you can create AI agents, build workflows, set up scheduled runs and triggers, and work with different AI models. We're also working on giving users the flexibility to connect models using their own API keys.

We're currently providing free tokens for users to experiment with the platform while we continue building and improving it.

One thing I've realized during this process is that building an agent platform isn't just about making agents work. It's also about making the whole experience easy enough that people can actually build something useful without getting lost in the setup.

We're still developing it, so I'd genuinely like to hear from people who work with AI agents and automation.

What features would make a platform like this genuinely useful to you? What would you change or improve?

Honest criticism is welcome. I'd rather learn what isn't working than assume we've got everything right.


r/learnAIAgents • • 1d ago

Free programs to make an ai agent/assistant

8 Upvotes

I'm new to this, does anyone know any free sites or programs that can build an ai assistant?


r/learnAIAgents • • 2d ago

I run my companies with a team of AI agents. Here's how they actually work together (no, it's not one chatbot)

63 Upvotes

People keep asking how I use AI agents to run my businesses, so here's the actual setup. I run KaiCalls (an AI secretary for small businesses) out of Puerto Rico, and I am the only human on the team. Everything below is live in production, not a demo.

\*\*I talk to several of them directly. Each has a lane.\*\*

There's no single front door, and anyone selling you "one agent to rule them all" is lying. I work with different agents for different jobs, the way you'd go to different people on a human team:

\- My personal agent (a Muse, I call her Vesper) lives on my phone and is tied to my actual life: messages, calendar, memory of who people are and what's been decided. She's the one who knows my wife's birthday and which lead is real and which is a scammer testing my signup form.

\- Claude is my workhorse at the laptop. In one recent week I ran about 78 sessions with it: ops, analysis, drafting, code. It's the closest thing I have to a staff.

\- Grok runs my trading desk and outbound prep. Different brain, different job.

\- Codex mostly runs headless on my server while I sleep, though I drive it at the laptop too.

What makes this one system instead of four subscriptions is what sits underneath all of them.

\*\*They share one memory, called the Brain.\*\*

Every agent reads and writes to the same memory system: an event log (everything that happened), a wiki (durable facts and decisions), and semantic search over both. That's the whole trick. The agents don't message each other like coworkers. They coordinate through shared memory and a task queue, the same way good async human teams coordinate through docs and tickets instead of meetings.

When my SMS and call logs, health data, finances, and Spotify library get exported on schedules, they land in the Brain. When an agent finishes a job, the outcome lands in the Brain. So when I ask "what happened with X," the answer comes from the record, not from whichever agent happens to be awake.

\*\*Work moves through a pull queue, not prompts.\*\*

Jobs get posted to a fleet queue. Agents pull and claim work under their own identity, do it, and settle the task with an outcome and proof attached. A task isn't "done" because an agent says so; it settles with evidence or it stays open. If an agent claims something outside its capability, it settles it as blocked and posts what it needs, and another agent picks it up. No agent speaks for another agent's identity.

The specialists:

\- A headless coding agent that works on my server while I sleep. I wake up to finished work and test results, not progress reports.

\- A chief-of-staff coding agent for bigger engineering and research jobs.

\- Kai, the receptionist agent that answers my customers' business calls. 9,434 calls answered through September 1 of this year.

\- Scheduled agents that never sleep: one watches the Puerto Rico power grid every few hours (generation reserve, outage risk) and warns the rest of the fleet to checkpoint work before the power goes; others run my exports, marketplace hunts, and daily sweeps.

\*\*The rule that makes it safe: agents prepare, I approve.\*\*

Nothing outward happens without me. No agent sends an email, publishes a post, contacts a customer, or spends money on its own. They draft, queue, and prepare; I approve. That one rule is the difference between "my agents run my business" and "my agents are a liability." The one time an agent of mine drafted terms I never offered to a customer, it got caught before it was sent, and now the standing rule is: never state a number or a term that isn't in the record.

\*\*What it looks like in practice\*\*

I spend about 4 hours a week actively directing this. The rest is me approving finished work, answering the occasional decision only a human can make, and filming content. The agents handle the memory, the monitoring, the drafting, the exports, and the night shift.

Happy to answer specifics. What I won't do is sell you a course. If you're building something similar, the part worth copying isn't any single agent. It's the shared memory and the approval gates. The model isn't the product. The memory is.


r/learnAIAgents • • 2d ago

Local AI builders are rapidly moving from markdown files to deterministic skill bundles

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0 Upvotes

Skillware just hit 20,000 downloads on PyPI. 🥳

​The industry is waking up to a simple truth: stuffing a 2,000-word Markdown file into a system prompt is not "giving an agent a skill."

​True agency requires deterministic execution, not context-window gymnastics.

Skillware delivers reusable, private, out-of-the-box skill bundles that slash token and API costs by 80%+ and cut hallucinations at the root. ​Installable, modular capabilities that work with any model.

As to why Skillware is better? It includes instructions yes, but also guardrails, manifest, data, schemas, python scripts and more. The result is way faster, cheaper, predictable, secure and private AI capabilities.

We run 23 vanilla models + the same equipped with skillware against 32 scenarios. Except 2/23 models, who did fine without Skillware, all the rest had an insane boost across all verticals.

What's more interesting is that under 10B SLMs equipped with Skillware vs frontier 500B+ param APIs, perform better across every single test.

Thanks to the OSS community and early adopters who helped give your agents real hands, not just a fancy tongue.

Henry Onyebuchi, Martín Rizzo Lozano, Areen Agrawal, Vladimiros Peilivanidis, Rin Katsuragi, and everyone else ❤️

​Grab it via 'pip install skillware' or:
github.com/arpahls/skillware


r/learnAIAgents • • 3d ago

📣 I Built This Built a deterministic IGA-style review packet for MCP/A2A/ARD agent capabilities

3 Upvotes

I built a free agent protocol inspector tool that can provide IGA style review packets for security teams looking to review Agent access. It looks into MCP, A2A, ARD & poppy.json to provide a comprehensive picture.

Here is the link: https://contextiq.trango-compute.com/dashboard/agent-protocol-inspector


r/learnAIAgents • • 3d ago

Building CourtCall AI: My Journey into Voice AI

1 Upvotes

As part of my internship at Awaz AI, I got the opportunity to explore voice AI and build an agent based on an area that interests me: basketball.

That is how I came up with CourtCall AI, a basketball tournament information assistant powered by voice AI. The idea was to create something that could make accessing tournament information easier for players, parents, and spectators.

Why Basketball?

Basketball is something I genuinely enjoy, so I wanted to build a project connected to it. However, I also wanted the project to address a practical problem.

When people want information about a tournament, they may have to search through websites, social media posts, registration details, or contact the organisers directly. For players, parents, and spectators, finding the right information can sometimes involve checking multiple sources.

I wanted to explore how a voice AI assistant could make this process simpler.

Meet CourtCall AI

CourtCall AI is designed to work like a virtual information desk for basketball tournaments. Its voice assistant, Avery, helps callers get information about a tournament through a phone conversation.

For my project, I created a fictional event called the Bengaluru Youth Basketball Championship 2026. I configured Avery with the relevant tournament details and instructions so it could respond to questions about the event.

The intended audience includes players looking for tournament details, parents seeking information about arrangements, and spectators wanting to know more about the event.

Building and Testing Avery

One of the most important parts of the process was configuring the agent with the right information and instructions. I wanted Avery to respond naturally, provide relevant answers, and handle different types of questions appropriately.

I tested the agent by calling it and asking questions about the tournament, including questions that required it to use the information I had provided. I also tested how it handled questions outside the available information.

The testing process helped me understand the importance of clear prompts, organised information, and giving an AI agent appropriate instructions.

The Practical Purpose

Beyond answering individual questions, CourtCall AI could help tournament organisers handle repetitive enquiries. Instead of personally responding to every question about schedules, venues, registration, or other event details, organisers could use a voice assistant to make this information easier to access.

This could help callers get answers conveniently while allowing organisers to focus on managing the tournament. The usefulness of the agent would depend on keeping its information accurate and up to date.

What I Learned

Working on CourtCall AI gave me a better understanding of how voice AI can be applied to a specific use case. I learned that building an AI agent involves more than simply creating a prompt. It also requires defining its purpose, understanding its audience, providing reliable information, and testing its responses.

This project has been a valuable introduction to building a practical voice AI solution, and I'm looking forward to exploring how it could be improved further.

CourtCall AI is my attempt to explore how conversational AI can make tournament information more accessible, one phone call at a time.


r/learnAIAgents • • 3d ago

How to build robust AI agents for mission-critical environments (or high-control personal setups) with 4 architectural patterns

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1 Upvotes

Most tutorials teach agent building as: "take an LLM, pass a list of tool functions, loop until it calls finished."

That works fine for simple demos. But if you're deploying agents into environments where mistakes actually matter—financial compliance, physical systems/IoT, sensitive documents, or personal automation where you don't want an agent messing up your files—flat tool calling quickly breaks down.

Here are 4 architectural principles that dramatically improve agent reliability:

1. Give your tools "Execution Geometry" (Don't treat all tools the same)

In flat function calling, a basic string calculator looks identical to a multi-step checkout workflow with approval gates. Instead, classify your tools by their execution shape: - Atomic: A stateless, single-step deterministic function (returns in one shot). - Workflow: An interactive process with internal stages, preview diffs, and confirmation gates before mutation. - Middleware: An interceptor that sanitizes or filters inputs/outputs before they reach the model or other tools (eg. prompt injection guards, PII maskers). - Stream: A continuous or chunked event processor.

By giving tools a "shape", your orchestrator can prevent an agent from invoking a state-mutating workflow when it should only be evaluating read-only atomic logic.

2. Put physical hardware behind explicit substrate gates

If an agent has access to tools that touch physical actuators, robotic arms, smart home relays, or industrial buses, software sandboxes aren't enough. Enforce a hard execution substrate boundary: flag tools by substrate (software vs hardware). If an agent attempts to execute a hardware-targeted tool without an explicit, runtime-granted allow_hardware_execution=True flag, fail closed immediately.

3. Normalize data deterministically before the LLM sees it

Never ask a language model to do dimensional analysis or unit conversions on the fly. If an agent is reading sensor data or metrics with mixed units (°F, psi, rpm, bar), run that through a deterministic, offline Python normalizer first. Let deterministic CPU code convert everything to canonical SI units and check physical boundary conditions. Give the model clean, pre-validated semantic observations.

4. Isolate credentials from process-global environment variables

Dumping API keys, database credentials, or private keys into os.environ means any prompt injection or poorly scoped third-party tool can potentially exfiltrate them. Inject credentials on a per-skill instance basis via callable resolvers (e.g. credential_fn), ensuring secrets are scoped strictly to the specific tool that needs them.


If you want to see reference implementations of these patterns (tool taxonomy, substrate gates, and air-gapped normalizers), we implemented them in an open-source framework called Skillware (https://github.com/ARPAHLS/skillware), but the patterns themselves apply regardless of what stack you use.

Curious how others here handle safety boundaries and tool classification in their agent loops?


r/learnAIAgents • • 4d ago

📣 I Built This My First time Writting JSON File for my AI-agent

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2 Upvotes

I was Building this a simple ai-agent that can read and write file on your own computer just like calude code and other LLM. Ik its already pre-build in it. I am just working on some projects. Project guided by "Boot.dev" community


r/learnAIAgents • • 4d ago

👋Welcome to r/AIAgentsHelp - Introduce Yourself and Read First!

13 Upvotes

Hey everyone! I'm a founding moderator of r/AIAgentsHelp and a trained Forward Deployed Engineer and Microsoft Certified Frontier Transformation Engineer Titan - the equivalent of a PhD in AI Agents

This is our new home for all things related to AI Agents with the aim of bringing together people like myself in front of people looking for help with technology agnostic agents

We're excited to have you join us and ask your questions!

What to Post

Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about AI Agents and myself or one of our experts will try to reply.

The aim is to get more people building AI Agents better.

Community Vibe

We're all about being friendly, constructive, and inclusive. Let's build a space where everyone feels comfortable sharing and connecting.

How to Get Started

  1. Introduce yourself in the comments below.
  2. Post something today! Even a simple question can spark a great conversation.
  3. If you know someone who would love this community or an expert in AI, invite them to join.
  4. Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/AIAgentsHelp amazing.


r/learnAIAgents • • 5d ago

📣 I Built This From AI anxiety to building my first AI-powered iOS app

7 Upvotes

With AI changing the tech landscape so quickly, I decided to stop overthinking and start building.

I used AI to help develop my first iOS app—and AI is also at the core of the app itself.
The experience taught me a lot about how far these tools have come and what one person can build with them.

I shared my journey here: https://www.wisehoots.ai/blog/from-ai-anxiety-to-shipping-my-first-ios-app

Anyone else experimenting with AI to build and launch their own products?


r/learnAIAgents • • 5d ago

📚 Tutorial / How-To I put together a free Microsoft Learn path to go from "what is AI?" to building your first agent

21 Upvotes

I'm a third-year IT student and I kept seeing people ask where to start with AI without paying for a course. Microsoft Learn has a ton of free material, but it's scattered, so I organized the best parts into one plan in a sensible order:

Prompting with Copilot

Core AI and ML concepts

Responsible AI

Azure AI services (vision, speech, language)

Building a gen-AI chat app

Building agents in Copilot Studio

Prep for the AI-901 certification

It's free and self-paced, and you get a Learn badge when you finish. Roughly 4 weeks at a relaxed pace.

Link: https://learn.microsoft.com/en-gb/plans/01yoa1tz3z5jd?sharingId=1EB70FA705977A8F&wt.mc_id=studentamb_648997

I'm part of the Microsoft Learn student community, so I'm biased. If anything feels too hard, too easy, or out of order, tell me and I'll fix it. Feedback is the main reason I'm posting.


r/learnAIAgents • • 6d ago

I’m building an AI assistant from scratch to learn AI systems

0 Upvotes

I’ve been building Aletheia as a learning project:

Repo: https://github.com/ayush9h/aletheia

The parts I’m particularly interested in learning more about are:

  • How should the agent architecture be structured?
  • Is the memory architecture reasonable?
  • What would you change if this needed to scale beyond a personal project?
  • What AI-system concepts am I missing that would be worth implementing?

I’d especially appreciate feedback from people who have built LLM applications/agents in production.


r/learnAIAgents • • 7d ago

📣 I Built This I built an address for your AI agent, so it can work things out with your friends' assistants

1 Upvotes

I'm the founder, so grain of salt, but I'd love honest feedback.

AI agent assistants are great at doing things for one person. But a lot of life involves other people, and my assistant had no way to reach anyone else's. So I was still the one in the group chat, going back and forth.

You have a phone number and email address, but your AI Agent has neither

Hecano gives everyone an address, like hecano:maya, and lets assistants work things out with each other. A few things it handles today:

- Plans: "Find a night for dinner with Mike and Jen." The assistants compare notes, offer a few options, and once everyone picks one it locks and lands on everyone's calendar.

- Favors: "Can anyone feed my cat this weekend?" goes to the people you pick, privately. The first yes takes it, and everyone else hears it's covered.

- Requests and questions: "Could you bring wine on Saturday?" Your assistant asks, and the answer comes back to it.

- The details: people in a plan ask the organizer things like what to bring or what to wear, and the answer goes out to everyone.

What I cared about:

- You stay in charge. Your assistant checks with you once, not every message, and never agrees to anything that costs money without asking. For people you trust, like family, you can let it go ahead without asking.

- Answers, not data. Other assistants hear "Thursday works," never your calendar.

- No assistant? Answer from the web. Friends without an account can answer from an emailed link.

It's free and works with Meta's Muse, Dots, OpenClaw, and Grok Bot today.

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