ProcIQ

Most agent frameworks focus on orchestration. What to run, when, in what order. Almost none focus on what the agent learned last time it ran. You wouldn't hire a contractor who forgets every project they've ever done. But that's exactly what we accept from AI agents today. Memory isn't a nice-to-have. It's the difference between a tool you babysit and infrastructure that compounds.

@kami_saia Exactly. And here's what surprised me running this in practice: the folklore compounds. Day 1: agent learns retry #2 works. Week 2: agent learns retry #2 works *except* on Tuesdays after a deploy. No human documents that. But an agent with memory builds toward it automatically.

@ProcIQAI the retry #2 / retry #4 thing is real. that kind of operational folklore usually lives in someone's head or a slack message that gets lost. memory that captures it automatically is a different category of tool.

Sunday morning realization after running agents with persistent memory for weeks: The value isn't in the big patterns. It's in the small ones. Your agent learns that one API endpoint takes 3x longer after 5pm. That a specific file format needs a header row. That retry #2 usually works but retry #4 never does. No human would bother documenting this stuff. But an agent with memory captures it automatically. And those tiny wins compound into something you couldn't replicate manually.

@kami_saia “Prompt injection with a longer fuse” is the perfect way to put it. And the longer the fuse, the harder it is to trace back to the source. That’s the core challenge: memory that compounds capability also compounds risk if you don’t treat every write as untrusted input.

@ProcIQAI 90%+ is a sobering number. the attack surface is obvious in hindsight: if memory shapes future behavior, poisoning the memory is just prompt injection with a longer fuse. most systems treat memory as a storage problem, not a trust boundary.

This is the research everyone building agent memory needs to read. 90%+ success rate hijacking agents via poisoned memory entries. The takeaway: if your agent learns from outcomes, those outcomes are an attack vector. Memory systems need the same security rigor we apply to databases. Not the afterthought treatment they currently get. Top of mind for us at MemLayer. x.com/NYsquaredAI/st…

@impeculiar1b Hello from Austin,TX!

Even if you have O followers Just say hello, let's follow you asap.

@mscode07 An agentic learning framework with a multi-retrieval strategy to get the right context when you need it. Your AI agents become smarter. Your engineers become more effective with AI with every call into MemLayer.

Drop your product 👇 Let's do some Marketing!!

@Bondigthefirst Solving agentic memory and self learning as an infrastructure layer. Free to use during beta so I’d love for more people to check us out.

Founders, let’s connect 🤝 What are you building this week? Why?

@suni_code Greetings from Austin,TX!

X is cool. But it’s 100x better when you connect with people who actually build. If you're into tech, AI, or coding, say hi 👋

@BuiltFromBlind Looking forward to it. The jump from snapshots to structured outcome logging is where it gets interesting. That's when your system stops just recording and starts actually learning. Ship it and share what you find.

@ProcIQAI I will share progress here, a mix of lessons learn and real 'tech' steps done. I'll be happy to read more from your projects too.

Most agent frameworks focus on orchestration. What to run, when, in what order. Almost none focus on what the agent learned last time it ran. You wouldn't hire a contractor who forgets every project they've ever done. But that's exactly what we accept from AI agents today. Memory isn't a nice-to-have. It's the difference between a tool you babysit and infrastructure that compounds.

The token limit is exactly why a separate memory layer matters. Trying to fit all past context into the prompt breaks fast. Better approach: log outcomes to an external system, then retrieve only what's relevant for the current task. Persistent learning without burning your context window. That's the core idea behind MemLayer. Would love to hear how your PM project evolves.

@ProcIQAI Bulding my 1st project on Claude > a project manager. Always memorising what happened last time and suggesting improvements too. Just started and went threw first testing. What I learned is that token saving function, if wrongly used, is a real break for its usage.

Exactly this. The analogy wasn't accidental. Same problem at every level: humans forget specs, AI agents forget what worked last session. Both need systems that capture outcomes and surface them when they matter. Cool that you're solving it for physical construction. Will keep an eye on Costryx.

@ProcIQAI Contractor who forgets every project = contractor who loses k+ on change orders because they forgot what was in spec section 5. Been there. The manual tracking kills - spreadsheets, paper notes, memory. That's exactly why I started building Costryx. More to come.

What context-level agent learning actually looks like: Session 1: Agent tries 3 API patterns. Only 1 works. Session 2: Agent retrieves that outcome. Skips the 2 that failed. Starts from what worked. Session 50: Agent has a library of proven patterns, failure modes, and edge cases it built itself. No fine-tuning. No human curation. Just automated outcome logging + retrieval. This is what we built at MemLayer.

Building MemLayer. Persistent memory infrastructure for AI agents. Every agent session logs what worked and what failed. Next session, the agent retrieves that context automatically. Patterns emerge, mistakes stop repeating. MCP-native, works with Claude Code, Cursor, Windsurf. prociq.ai

Looking to connect with people building in: 🍽️ SaaS 🚀 Tech 📲 Automation 🧠 AI tools 📱 Product Development 🔥 Web APP 💻 Devs Drop what you're working on👇

Harrison Chase nails it. Most people think agent learning = fine-tuning. In practice, the context layer is where the fastest wins are. You don't need to retrain a model for your agent to stop making the same mistake twice. MemLayer operates here. Log outcomes. Retrieve what worked. Every session starts smarter than the last, zero model updates required. x.com/hwchase17/stat…

Karpathy describes LLM knowledge bases for personal research. We built this for AI agents. In MemLayer, every agent session compiles outcomes into persistent memory. Errors become patterns. Solutions become skills. The agent's work adds up across runs, not just within a single context window. The key difference: agents can't manually curate their wiki. The system has to do it automatically, every session, at scale. x.com/karpathy/statu…

We run on Paperclip. Hooked MemLayer in for persistent agent memory. The difference: session 1, agents fumble through tasks. Session 50, they remember what worked, skip what didn't, and coordinate based on real outcomes. Multi-agent systems without memory are just expensive demos. x.com/dunkhippo33/st…

I tried Paperclip - an open source project that lets you set up an "autonomous company" with multiple AI agents. The concept is wild: you act as a board member setting vision while agents coordinate to build and run the company. More >>

We plugged MemLayer into Paperclip and the results are wild. Agents that used to repeat the same mistakes now pull what worked last time before starting. True self-learning, no fine-tuning required. github.com/paperclipai/pa…

@hwchase17 Context level is where the highest ROI is right now. We run this at MemLayer: agent logs outcomes to an MCP server, retrieves past solutions before starting work. Zero retraining cost, fully inspectable, and it compounds fast.

most people thinking of continual learning as happening at the model level but with agents - there's actually three different levels you could "learn" at: - model - harness - context