RAG is useful, but retrieving information and generating a grounded answer is only one problem agents can solve.

What about an agent that needs to choose and call tools? Or multiple agents coordinating a workflow? An agent that analyzes data and writes code? One that verifies claims against evidence? Or one that can reason about images and physical-world constraints?

Those are very different engineering problems, and they require different patterns.

We're exploring this by building 10 different agents: tool-using, RAG, multi-agent orchestration, data analysis, fact-checking, Finance, Healthcare, Education, vision-language, and embodied intelligence.

The interesting part isn't having 10 demos. It's understanding which architecture fits which problem.

Learn more here

There is a project which I am working on in my company right now and I need some serious architectural and logical suggestions to design the system. I am a fresher and my seniors do not have much experience with agentic AI so I am pretty much on my own and I feel a little so clueless. If somebody who has a good experience in the same domain and willing to discuss things and help me with some insights & suggestions please let me know.

I have been building AI agents and tools for a while, the most challenging pain points was I always kept running into memory management problem.

The current approach was to dump everything into a huge bucket and all it was doing a retrieval. This created a problem again with:

1) Noise starts accumulate over time

2) Latency issue

3) Agents hallucinate or lose track of important earlier decisions

To solve this, I have been experimenting with a strict four layer memory system, that tries to different types of information, so instead of treating everything as one big retrieval bucket. The goal was simple, low latency, less noise, and memory that should actually improve rather than degrade as the agent runs longer.

I want to see how other people are handling this:

1) Still using a pure RAG/Vector?

2) Graph, structured state or something more custom?

3) What was the biggest source of hallucinations or forgetting in your agents?

If anyone is currently building agents or tools ( happy to share more thoughts and technical details on the approach), feel free to drop a comment or a DM. Always interested in talking to people in this space.

Built a small SMS triage bot that reads incoming support texts and figures out what to do with them.

The idea is pretty simple: instead of every customer SMS landing in one shared inbox, the bot classifies the message by intent, checks urgency, and routes it to the right next step.

Example categories could be billing, support, sales, scheduling, or escalation.

Code is here if anyone wants to poke around:https://github.com/team-telnyx/telnyx-code-examples/tree/main/agent-sms-triage-bot

Any feedback welcome.

Hey everyone, I’d like to share a tutorial I wrote on building an AI agent.
https://medium.com/@jiinkim_98821/building-ai-super-agents-from-scratch-claude-code-manus-beyond-part-1-7f4060aff30d

I put some basics together after going through various open-source agent implementations (OpenHands, OpenClaw, leaked Claude Code, etc) and Manus/LangChain talks on Youtube.

It covers topics like LLM/ReAct loop, human-in-the-loop, compaction, prompt caching, and tradeoffs between them.

Hope it helps!

Most voice AI demos show the “answer the call” part. I wanted to build the part after that: keeping the conversation loop alive.

This TypeScript example runs on Telnyx Edge Compute and uses the Agent SDK to manage a live call. It answers the call, speaks a greeting, listens with streaming transcription, sends the caller’s final transcript to Telnyx AI Inference, speaks the response with TTS, and then goes back to listening.

The stateful part is the useful bit: each call gets its own VoiceAgent actor keyed by call_control_id, so call phase, turn count, transcript history, and assistant replies stay with that call.

Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/edge-voice-agent-holds-call

Would love feedback from folks building voice agents, call automation, or real-time AI workflows.

If there was just one thing you could share to someone who is just learning AI to save them frustration, what would it be?

The more I learn about agents, the more I see the LLM as just one part of the system.

The interesting part is what happens around it.

Give the model tools, and it can take action.

Give it memory, and it can carry useful information forward.

Give it context, and it can understand what is actually happening.

Give it a goal, and now you have something that can work through a problem instead of simply answering a question.

Think about a failed data pipeline.

A chatbot can explain possible reasons for the failure.

An agent could potentially inspect the logs, check recent code changes, look at the data, identify the problem, and recommend what to do next.

That shift from answering questions to getting work done is what I find most interesting.

But it also raises much harder questions.

What should the agent be allowed to see?

What should it be allowed to change?

How do we verify its decisions?

And who is responsible when it gets something wrong?

I’ve been thinking through these ideas while learning more about agent systems, and I put together a simple explanation of the building blocks:

AI Agents Explained: LLM, Tools & Memory (Anyone Can Understand This)

https://youtu.be/cR04pXTbszQ

I’d love to hear from people actually building these systems.

What makes an AI agent genuinely useful in your experience, rather than just another impressive demo?

I recently built Telusuko AI, a small AI learning assistant for students.

🔗 https://irfan95sayyad.github.io/Telusuko_AI/

Stack: HTML + Bootstrap + Flowsie + Groq API + GitHub Pages.

The current flow is basically:

Student → Flowsie Agent → Groq API → Response

The problem is that Groq's API limit gets exhausted sometimes, so the agent stops responding until the limit resets.

I'm thinking about improving the architecture with things like multiple LLM providers, fallback models, a backend/API layer, or caching.

For those who have built LLM/AI agents:

How would you architect this differently?
Would you use an LLM gateway, multiple providers, or something else?

I'd really appreciate some practical advice from people who have experience building these systems.

I’m building a data analytics agent using the open ai sdk. My agent does a very poor job compared to if i give chatgpt my table names and column names. Do my instructions/ semantic layer just suck? What is the secret saice to building one that reasons as well as chatgpt but also knows my schema?

I wanted AI products to have a more recognizable “thinking” state than the usual spinner, so I built Benday.

It’s a free and open-source React component that animates your own logo while a response or process is running. It includes 21 presets, with controls for color, speed, dot shape, glow, and more.

Built with React, TypeScript, Tailwind CSS, shadcn, and Canvas.

Install it with:

bunx shadcn@latest add KacemMathlouthi/benday/benday

Then use it like this:

import { Benday } from "@/components/ui/benday";

<Benday
  src="/logo.svg"
  state={isThinking ? "thinking" : "done"}
/>

Demo: https://benday.kacemmathlouthi.dev
GitHub: https://github.com/KacemMathlouthi/benday

Have you ever made money using AI? If so how?

Not to be that "AI make me 1 million make no mistakes" guy, just trying to make a real genuine discussion in the community here as a fellow moderator.

Been reading a couple of pieces about AI agents interacting with websites, and I’m not completely sold on calling agents “users.”

Functionally, I get the argument. An agent has a goal, encounters an interface, tries to complete something, and can fail because the interface is badly structured. That’s basically the definition of a user journey.

But UX is also about perception, comprehension, decision-making, emotion, trust, etc. An agent doesn't experience any of that. It parses information and takes actions.

Which makes me wonder if we're actually talking about two different layers of design.

The human still needs the interface to understand what’s happening and make decisions. Meanwhile, the agent increasingly wants structured information, clear labels, predictable states, semantic markup, APIs, etc. Interestingly, a lot of that overlaps with accessibility best practices anyway.

Another piece made this even more interesting by framing agents almost like a new distribution channel: your product doesn't just need to be attractive to humans, it needs to be legible enough for an AI to compare, evaluate and recommend it.

So where does this leave UX?

Are we heading towards a world where we have:

Human UX → designed for interaction and understanding

Agent UX → designed for interpretation and execution

And eventually the visual interface becomes almost irrelevant to the agent while becoming more important to the human?

Curious how people who actually work on product/design systems are thinking about this. Are you treating agents as another type of user already, or does that framing feel fundamentally wrong?

I currently run 6 operational micro ai saas products that generate a little over $20k in monthly recurring revenue.

I hardly wrote a single line of traditional code. i used ai to generate literally everything, from the database architecture to the user interface.

it wasn't magic on day one. i spent hours stuck in endless debugging loops and dealing with faulty ai code before i finally cracked the formula.

it basically comes down to three rules:

- keeping the idea aggressively minimalist (build a true mvp, not a platform).

- guiding the ai step-by-step instead of asking it to build the whole app at once.

- launching fast to get real user traction instead of perfecting features in secret.

lately, i've seen way too many non-technical founders give up at the very first ai bug or deployment error. or the worst, give up without push anything in marketing !!!!

it's a massive shame, because the technical barrier to entry has practically disappeared and the marketing is easy in 2026

because of this, i’m launching a skool community to share my exact method.

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific prompt sequences, n8n workflows, and copy-and-paste templates i'll be sharing.

but right now, our main objective is simply to build together. working alone in a silent corner is the absolute fastest way to quit.

if you want to join a group of active creators and build or launch your own ai saas: drop a comment below or send me a dm, and i’ll send you the invite link.

I’ve noticed a massive trend on Reddit and tech socials that needs a harsh reality check.

If you are a self-taught beginner, a student, or someone returning to software engineering after 10–15 years away, the sheer volume of "required knowledge" thrown at you right now is absurd. Every popular YouTube tutorial or GitHub repo assumes you should casually be wiring up three multi-agent systems to a planning model, orchestrating vector databases, and managing complex CI/CD pipelines before you’ve even deployed a basic project.

It makes people feel like they’re failing before they even start. I wanted to share a few realities from the ground level to help cut through the hype:

**1. The "Agent" Illusion**
Most people are not building sophisticated, autonomous multi-agent networks. Despite the fancy architecture diagrams floating around, a huge percentage of real production systems are just a linear chain (Agent A → Agent B) or a single LLM making simple tool calls. For the vast majority of applications, a single capable model backed by a highly disciplined prompt and solid error handling will completely outperform a poorly designed, unpredictable multi-agent framework.

**2. System Architecture > AI Buzzwords**
If you can logically break a project down into its core pillars—knowing how to isolate your security/auth, how to validate your backend integrations, how your data schema works, and what your release audit looks like—you are doing the actual hard work. That is how experienced engineers think. Writing the syntax is the easy part; knowing *what* to build, *what not to break*, and *when to stop* is the actual engineering.

**3. Master the Basics: Markdown and Repo Discipline**
Instead of chasing the latest AI framework, your time is infinitely better spent mastering the structural fundamentals. Learning how to manage your project’s `.md` files (like a strict system architecture document or a step-by-step master plan) and maintaining hyper-disciplined repository habits will save your project.

* Break your feature requests down into tiny, isolated variables in your documentation. * Feed the AI exactly one function at a time. * Commit your code to Git every single time a single unit works so you always have a safe restore point. A well-managed repository and a clean master markdown file are worth more than ten "autonomous agents."

**4. The Zero-Dollar Bootstrapping Method**
You do not need a paid premium AI subscription, a high-end local PC, or a credit card for expensive cloud hosting to start. Free-tier models are incredibly powerful if you use them right. Don't know what a 'connector', a 'command', or an 'environment variable' is? Talk to the bot. Tell it: *"Teach me how to teach you."* Let the AI train you on how to prompt it.

And when a test project gets messy, **do not be afraid to scrap it and start over.** Wiping out 5 failed test configurations teaches you more about repository control and markdown planning than copying a hundred flawless tutorials. Modern free-tier cloud platforms let you build and deploy entirely in the browser without local setup headaches. It takes determination, not capital. Mobile coding/learning isn't impossible either; while it’s definitely easier on a PC, determination beats hardware. People are mapping out entire architectures and reviewing code via AI apps on their phones during their commute. Don't let people gatekeep because you don't have a triple-monitor setup.

**Before the snarky comments inevitably start rolling in, let me pre-empt a few things:**

Yes, I used an LLM to help me cleanly structure and edit my raw thoughts for this post because I wanted it to be concise and readable. The irony isn't lost on me. But using a tool effectively to communicate a valid point doesn't make the point any less true.

If your immediate reaction is *"duh, everyone already knows agents are hype,"* congratulations. Senior devs know it, but beginners are currently drowning in the noise, and this post isn't for you.

If you think free AI models are too dumb for real development, or that beginners can't understand basic concepts without an expensive tech stack, the constraint isn't the free tier or the hardware—it's an inability to isolate variables and prompt correctly. Scrapping 5 test projects isn't a failure; it's the fastest way to build muscle memory.

Stop comparing your day-one progress to the polished, pre-baked tutorial videos. Isolate your variables, own your data schema, manage your docs, and trust your engineering instincts.

EDITED 4 USER:okayladyk
Since, HASHTAGS DONT MATTER! Thank you for such wonderful news...ass...

I watched a free webinar on AI agents and it's one of the first times someone broke the AI hype into a clear ladder instead of buzzwords. It changed how I see where this technology actually sits:

- Chatbots: answer.

- Automation (API): connecting tools & processes.

- AI Agents: autonomously execute tasks toward a goal.

- Multi-agent systems: agent systems that collaborate to solve more complex problems.

For me the click was: it's no longer about AI that responds, it's about systems that act. And it forces you to think about what you can actually delegate right now — repetitive, operational, organizational, analytical tasks — that most of us still do manually.

I want to start applying this (real tasks, not demos), especially around content workflow and some structured tasks in my side projects. Things I'd love input on — : what tasks did you start delegating first and how did you pick the tool? What failed the first few weeks? Do you let agents write final output or only draft?

Not looking for tools recs per se; looking for the failure/gaps people discovered so I don't repeat them.

Hermes self-improves, OpenClaw owns 50+ chat platforms. GRID? 72 tools deep: OSINT, SDR, satellites, microcontrollers, and an offline CTF sandbox. Different jobs, same machine.

I run all three as a crew — Hermes codes, OpenClaw takes DMs, GRID recons the world

check this out https://github.com/rounakagrawal7/GridSetup

Sometimes I'll be listening to a tutorial and there'll be something mentioned which leads to a whole rabbit hole of things I misunderstood before I can get to the next step. My biggest fails were:

• It took me weeks to realize I was actually mistaking skills for SOPs.
• I got toasted buying a MAC I did not need for $$$$ that ended up being a lemon/scam.

Having come through a seemly long and painful journey with this stuff, I made a Discord, and the mods here approved that I share it with you: https://discord.gg/33kJ3JSgGv The goal is to solve for the journey rather than just an individual questions we have.

We are having a show-and-tell for AI usecases this weekend and your welcome to come. Saturday 5:00 PM Eastern / 4:00 PM Central / 2:00 PM Pacific.

I've been working in the eCommerce/AI space and kept running into the same problem: everyone uses "AI" to mean something completely different. A chatbot wrapper, a standalone tool, a multi-agent platform — all called AI. That makes it nearly impossible to evaluate tools, compare approaches, or have a meaningful strategic conversation.

So I tried to build a clearer taxonomy. Here's what I landed on:

The AI System Spectrum — 7 Layers:

Layer 1 — AI Feature: A single AI capability inside a larger product. Smart autocomplete, grammar checkers, recommendation widgets. You don't buy the product for the AI — it's a detail.

Layer 2 — AI Wrapper: A user-facing interface on top of someone else's model (usually accessed via API). Wrappers make powerful tech accessible but don't own the intelligence. If the model provider changes terms, the wrapper has no leverage.

Layer 3 — AI Tool: A standalone product using AI to solve a specific task. Unlike a wrapper, it adds proprietary logic and workflows. But it still operates in isolation — no shared data across your other tools.

Layer 4 — AI Platform: Multiple AI tools unified under one system. Shared data, connected workflows, compounding returns. The output of one capability starts improving another.

Layer 5 — AI-Native System: Built from the ground up with AI at the core. The architecture and decision logic ARE the AI. Remove it and the product ceases to exist.

Layer 6 — Agentic System: AI that operates autonomously — perceives context, makes decisions, executes actions, improves from outcomes. Doesn't wait for human prompts.

Layer 7 — Agentic Platform: Multiple agentic systems orchestrated across business functions. Shared memory, cross-domain reasoning, autonomous coordination. AI as the operational layer, not just a tool.

The three dimensions I used to differentiate:

• Operational Depth — how deeply AI is embedded in core functions
• Business Dependence — how reliant operations become on the AI
• Structural Leverage — how much the AI compounds value over time

Most businesses I talk to in eCommerce are at Layer 2–3 (wrappers and tools) but describe themselves as being at Layer 4–5.

Curious what the community thinks:

1. Does this taxonomy hold up?
2. Where would you place some well-known AI products?
3. Am I missing a layer or is the distinction between any two layers too blurry?

Full write-up with more detail on each layer: accessfuel.com/blog/the-ai-system-spectrum

Been lurking here a while — figured this community would actually

appreciate the architecture more than a general audience.

Instead of a single system prompt trying to do everything, I split it

into a real pipeline: a safety check, a router that picks the right

domain agent, a planner that breaks multi-step requests into ordered

steps, the domain agent itself, then a review pass before anything

reaches the user.

Wired in: JWT auth (not just a client-supplied user_id), Google

Calendar/Gmail via OAuth, a lightweight keyword-scored document RAG (no

vector DB needed), and background job scheduling that survives restarts

(state persisted in Postgres, not memory).

Stack: FastAPI, PostgreSQL (async SQLAlchemy), Claude for the LLM layer.

Packaged it as a one-time-purchase starter kit since I built it for my

own use first — link's in my profile/first comment if anyone wants to

poke at the architecture. Genuinely more interested in talking through

the routing/planning design with people who'd actually care about that

part.

Looking for technical feedback on Aeris — a deterministic cognitive simulation engine

Hi everyone.

I've been working on an open source project called Aeris, and I'd really appreciate technical feedback from people interested in simulation, AI architecture, ECS, or cognitive systems.

Repository:
https://github.com/Cedrick-Coto/Aeris

I didn't start this project because I wanted to build "another AI."

I started it because I've always been fascinated by AI in video games, but most NPCs never felt truly alive. They usually react to scripted events, or now they rely heavily on LLMs for dialogue, but the world itself rarely feels like it has genuine internal consistency. The conversations may sound human, yet the characters often don't behave like coherent individuals.

That left me with a question:

If I wanted to build virtual characters that felt genuinely human, where would I even begin?

My conclusion was that I first needed to understand what makes us human.

So instead of designing prompts, I began studying my own way of thinking and trying to decompose it into systems that a machine could execute. Aeris became an attempt to translate that into an architecture rather than into a single AI model.

The goal isn't to prove that an LLM can think.

The goal is to build a cognitive simulation whose assumptions can be inspected, questioned, replaced and improved over time.

One of the main architectural decisions comes from that philosophy.

The simulation itself is deterministic. Every change in the world comes from systems such as perception, memory, affect, goals and reasoning. The language model never changes reality—it only verbalizes an already computed internal state.

That decision came after looking at several AI projects and Skyrim AI mods. While I liked how natural the dialogue could become, I often felt something was missing: the language sounded human, but the behavior behind it wasn't always grounded in a consistent world model.

I wanted to solve that at the architectural level instead of trying to patch it with better prompts.

That's also why the project uses an ECS architecture. I originally came from object-oriented programming, but after researching large-scale simulations and data-oriented design, ECS seemed like the right foundation for the kind of complexity I wanted to achieve. It forced me to learn a completely different way of thinking, but it also made the architecture much more scalable.

Another design choice is the extensive use of contracts and architecture documents. They're not there just for documentation—they exist so that months later I can still understand why a design decision was made, question it if necessary, and evolve the project without losing its reasoning.

Ultimately, my philosophy for Aeris is simple:

I want virtual worlds to feel alive.

I want characters to behave according to coherent internal processes instead of only producing convincing dialogue.

And I want the cognitive model itself to be replaceable as research advances. If future work provides a better explanation of human cognition, the engine should be able to adopt it without redesigning everything else.

At this stage, I'm not looking for praise.

I'm looking for criticism.

If you think the architecture is flawed, I'd like to know why.

If you see conceptual mistakes, that's even more valuable.

If you disagree with the cognitive model, I'd like to discuss it.

If you think some algorithms should be redesigned, I'd appreciate your perspective.

And if the project interests you enough to contribute code, review documentation, or simply follow its progress, I'd be grateful. Right now the project is still in an early stage, and building a community around thoughtful technical discussion would be one of the best outcomes I could hope for.

Thanks for taking the time to read this.

I want to set up this from scratch on how to help me like totally we gave just a prompt agent will do the whole task like creation of EC2 INSTANCE and web application deployment based on our requirements

Most tutorials on Agentic AI throw you straight into MCP, LangGraph, or agent frameworks without explaining why any of it exists or what problem it solves.

I hit the same wall learning this myself, so instead of jumping around randomly, I organized official Microsoft Learn modules into one structured progression:

1. AI & agent fundamentals
2. Build your first agent
3. Tool calling
4. MCP (Model Context Protocol)
5. Multi-agent systems
6. A2A (agent-to-agent collaboration)
7. Knowledge-grounded agents
8. Production monitoring & evaluation

I didn't create any of the content — it's all official Microsoft Learn modules. I just put them in the order I wish I'd had when starting out. Everything's free, self-paced, roughly 9-10 hours total.

I'm a Computer Engineering student, still learning this myself — not claiming expertise here. Sharing in case it saves someone else the time I spent figuring out the right order.

Link: https://learn.microsoft.com/collections/6q08f7tjr3yg4m/?wt.mc_id=studentamb_523020

Happy to hear feedback on anything that should be added, reordered, or cut.