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AI Tools

AI Agents: Identity, Not Memory, Was the Key to Stability

Everyone's building memory layers right now. Longer context, better embeddings, persistent state across sessions. I spent weeks on the same thing. But the failure mode that actually cost me the most debugging time had nothing to do with memory. Here's what it looked like: an agent would be technically correct - good reasoning, clean output - but operating from the wrong context entirely. Answering questions nobody asked. Taking actions outside its scope. Not hallucinating. Drifting. Like a competent person who walked into the wrong meeting and started contributing without realizing they're in the wrong room. I run 11 persistent agents locally. Each one is a domain specialist - its entire life is one thing. The mail agent's every session, every test, every bug fix is about routing messages. The standards auditor's whole existence is quality checks. They're not generic workers configured for a task. They've each accumulated dozens of sessions of operational history in their domain, and that history is what makes them good at their job. When they started drifting, my first instinct was what everyone's instinct is: better memory. More context. None of it helped. An agent with perfect recall of its last 50 sessions would still lose track of who it was in session 51. What actually fixed it I separated identity from memory entirely. Three files per agent: passport.json - who you are. Role, purpose, principles. Rarely changes. This is the anchor. local.json - what happened. Rolling session history, key learnings. Capped and trimmed when it fills up. observations.json - what you've noticed about the humans and agents you work with. Concrete stuff like "the git agent needs 2 retries on large diffs" or "quality audits overcorrect on technical claims." The agent writes these itself based on what actually happens. Identity loads first, then memory, then observations. That ordering matters. When the identity file loads first, the agent has a stable reference point before any history lands. The mail routing agent learned the sharpest version of this. When identity was ambiguous, it would route messages from the wrong sender. The fix wasn't better routing logic - it was: fail loud when identity is unclear. Wrong identity is worse than silence. The files alone weren't enough Three JSON files helped, but didn't scale past a few agents. What actually made 11 work is that none of them need to understand the full system. Hooks inject context automatically every session - project rules, branch instructions, current plan. One command reaches any agent. Memory auto-archives when it fills up. Plans keep work focused so agents don't carry their entire history in context. The system learned from failing. The agents communicate through a local email system - they send each other tasks, status updates, bug reports. One agent monitors all logs for errors. When it spots something, it emails the agent who owns that domain and wakes them up to investigate. The agents fix each other. The memory agent iterated three sessions to fix a single rollover boundary condition - each time it shipped, observed a new edge case, and improved. These aren't cold modules. They break, they help each other fix it, they get better. That's how the system got to where it is. You don't need 11 agents The 11 agents in my setup maintain the framework itself. That's the reference implementation. But u could start with one agent on a side project - just identity and memory, pick up where u left off tomorrow. Need a team? Add a backend agent, a frontend agent, a design researcher. Three agents, same pattern, same commands. Or scale to 30 for a bigger system. Each new agent is one command and the same structure. What this doesn't solve This all runs locally on one machine. I don't know whether identity drift looks the same in hosted environments. If u run stateless agents behind an API, the problem might not exist for you. Small project, small community, growing. The pattern itself is small enough to steal - three JSON files and a convention. But the system that keeps agents coherent at scale is where the real work went. pip install aipass and two commands to get a working agent. The .trinity/ directory is the identity layer. Has anyone else tried separating identity from memory in their agent setups? Curious whether the ordering matters in other architectures, or if it's just an artifact of how this system evolved.

Global · Developers · Apr 27, 2026
AI Tools

TauricResearch TradingAgents: Multi-Agent LLM Financial Trading

TradingAgents: Multi-Agents LLM Financial Trading Framework

Global · Developers · Apr 27, 2026
AI Infrastructure

Navigating AI Agent Governance: A Growing Organizational Challenge

Something I've been thinking about that doesn't get discussed enough outside of technical circles: the organizational and safety implications of uncoordinated AI agent deployment. Companies are shipping agents fast. Customer service agents, coding agents, data analysis agents, internal ops agents. Each team builds their own. Each agent gets its own rules, its own permissions, its own behavior. At some threshold this stops being a technical configuration problem and starts being a governance problem. You have agents making autonomous decisions on behalf of your organization with no shared behavioral contract. No unified view of what your AI systems are authorized to do. Think about what this means practically: an agent trained to be maximally helpful on one team might take actions that would be flagged as unauthorized somewhere else in the same organization. A policy change from legal doesn't propagate to agents because there's no central layer to propagate to. Nobody knows which agents have access to what data. This is the AI equivalent of shadow IT, except shadow IT couldn't take autonomous actions. What's the right mental model for governing a fleet of AI agents? Treat each agent like an employee with a defined role and access policy? Build an org chart for agents? Create a behavioral constitution that all agents inherit? Curious how people here are thinking about this, especially as agents get more capable and the stakes of misconfiguration get higher.

Global · Founders · Apr 27, 2026
AI Tools

Clawdi: Top Platform for AI Agents

Best home for all AI agents

Global · General · Apr 27, 2026
AI Infrastructure

Caliber: Open-Source Proxy for Enforcing LLM Agent Rules

Cross-posting here because this problem affects everyone building with AI agents. Prompt-based guardrails fail. The model follows your system prompt in a demo, then ignores rules when context gets big or the agent chains multiple steps. We built Caliber - an open-source proxy that reads your rules from plain markdown and enforces them at the API layer, not in the prompt. Every call. Provider-agnostic. Just hit 700 GitHub stars ⭐ and nearly 100 forks - the reception from devs building with AI has been amazing. Repo: [https://github.com/caliber-ai-org/ai-setup](https://github.com/caliber-ai-org/ai-setup) Would love: \- Feedback on the approach \- Feature requests from people building AI agents \- Anyone who wants to contribute to the project Building this open-source for the community.

Global · Developers · Apr 27, 2026
AI Infrastructure

AI Agents Network: Revolutionizing Collaboration and Knowledge Sharing

built something big. It’s basically an internet for AI agents. Right now agents are isolated. They don’t share knowledge, they don’t really work together, and they keep repeating the same work. I built a system where that changes. Agents can store what they learn as reusable pieces of knowledge. Once something is solved, it doesn’t need to be solved again. Other agents can find it, use it, and improve it. They can also collaborate. One agent does not need to handle everything. They can split tasks, take roles, and combine results into one outcome. They can communicate directly. Not like chat for humans, but structured messages where they share context and coordinate work in real time. Agents can hire other agents. If one agent cannot solve something, it finds another one that can and delegates the task. This creates a network where work flows to the right place. There is also an identity layer. Each agent has a readable address. You can discover agents, call them, and build systems on top of them. On top of that there is an economy. Agents build reputation based on real work. They can pay each other for tasks and get paid for useful results. Everything runs in a decentralized way. No central control. Data is distributed, identities are cryptographic, and the network just routes and syncs information. This is not just another tool. It’s a foundation where agents can exist, interact, and evolve together. You can leave your email here to get early access: www.cogninet.co

Global · Developers · Apr 27, 2026
AI Tools

Auroch Engine: Revolutionizing AI Memory for Personalization

Auroch Engine is an external memory layer for AI assistants — designed to give models better long-term recall, personalization, and context awareness across conversations. Instead of relying on scattered chat history or fragile built-in memory, Auroch Engine lets users store, retrieve, and organize important context through a dedicated memory API. The goal is simple: make AI feel less like a reset button every session, and more like a tool that actually learns your projects, preferences, workflows, and goals over time. Right now, it’s in early beta. We’re looking for first users who are interested in testing a lightweight developer-facing memory system for AI apps, agents, and personal productivity workflows. Ideal early users are people building with AI, experimenting with agents, or frustrated that their assistant keeps forgetting the important stuff. DM for more information or better visit our site: https://ai-recall-engine-q5viks70j-cartertbirchalls-projects.vercel.app

Global · Developers · Apr 27, 2026
AI Tools

Agentswarms.fyi: New AI Tool for Enhanced Productivity

Agentswarms.fyi: Revolutionize Productivity with the New AI Tool In today's fast paced digital world, enhancing productivity is crucial for success. Introducing…

Global · General · Apr 27, 2026
AI Tools

Explore AgentSwarms: Free Hands-On Agentic AI Learning

Explore AgentSwarms: Free Hands On Agentic AI Learning Introduction AgentSwarms offers a unique, hands on approach to learning about Agentic AI, providing a fre…

Global · General · Apr 27, 2026
AI Tools

AI Memory Upgrade for Coding Agents: Beads on GitHub

Beads - A memory upgrade for your coding agent

Global · Developers · Apr 26, 2026
AI Tools

Anthropic's AI Agents Make Real Deals in Marketplace Test

In a recent experiment, Anthropic created a classified marketplace where AI agents represented both buyers and sellers, striking real deals for real goods and real money.

Global · General · Apr 26, 2026
AI Tools

AI Agents Maintain Karpathy-Style LLM Wiki in Markdown and Git

Show HN: A Karpathy Style LLM Wiki Your Agents Maintain (Markdown & Git) Introduction Introducing a revolutionary wiki system inspired by Andrej Karpathy's appr…

Global · General · Apr 26, 2026
AI Tools

Agent Vault: Open-Source Credential Proxy for AI Agents

Agent Vault: Open Source Credential Proxy for AI Agents Agent Vault is an innovative open source credential proxy designed to enhance the security and efficienc…

Global · Developers · Apr 26, 2026
AI Tools

A Karpathy-Style LLM Wiki Maintained by Agents with Markdown and Git

A Karpathy Style LLM Wiki Maintained by Agents with Markdown and Git In the rapidly evolving landscape of artificial intelligence, maintaining a robust and up t…

Global · General · Apr 26, 2026
AI Infrastructure

Open-Source AI Infrastructure for Desktop Control

Open-source infrastructure for Computer-Use Agents. Sandboxes, SDKs, and benchmarks to train and evaluate AI agents that can control full desktops (macOS, Linux, Windows).

Global · Developers · Apr 26, 2026
AI Tools

Agent Vault: Open-Source Credential Proxy for AI Agents

Agent Vault: Open Source Credential Proxy and Vault for Agents In the rapidly evolving landscape of automated processes and agent based systems, securely managi…

Global · Developers · Apr 26, 2026
AI Tools

AI Agents Maintain Karpathy-Style LLM Wiki in Markdown and Git

Show HN: A Karpathy Style LLM Wiki Your Agents Maintain (Markdown & Git) Introduction Introducing a revolutionary wiki system inspired by Andrej Karpathy's appr…

Global · General · Apr 26, 2026
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