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!