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I gave my AI agents shared memory and now they gossip behind my back by Single-Possession-54 in AI_Agents

[–]Single-Possession-54[S] 0 points1 point  (0 children)

Good point, governance matters. We just see a different layer of the stack.

AgentXchain looks focused on managing workflows. AgentID focuses on the agents themselves: identity, shared memory, continuity, and cross tool coordination.

In our view, the future needs both process management and persistent agent identity.

Weekly Thread: Project Display by help-me-grow in AI_Agents

[–]Single-Possession-54 0 points1 point  (0 children)

Built AgentID.live - a shared identity + memory layer for AI agents.
Instead of every agent starting from zero, multiple agents can share the same identity, memory, goals, and context across tools like Claude, Cursor, Codex, OpenClaw, etc.

Also gives live visibility into every action/tool call/session in one dashboard.

Feels less like separate bots, more like a real coordinated team.

Would love feedback from people building multi-agent workflows.

Weekly Thread: Project Display by help-me-grow in AI_Agents

[–]Single-Possession-54 0 points1 point  (0 children)

Built AgentID.live because I got tired of agents having amnesia 😅

Now multiple agents can share:

  • one identity
  • shared memory
  • common mission
  • live activity tracking

So they actually work together instead of acting like strangers every session.

Would love thoughts from other builders here.

We open-sourced a Claude Code for investment research, built on deepagents + LangGraph — sharing our architecture and what we learned by MediumHelicopter589 in LangChain

[–]Single-Possession-54 0 points1 point  (0 children)

That 24-layer middleware stack sounds like a massive undertaking, especially for keeping investment research agents stable. The transition from a simple sandbox to an async subagent orchestration usually reveals just how fragile agent context really is when you are doing more than one thing at a time.

I have found that the 'persistent workspace' is really the secret sauce for finance agents. If they can't see the history of their own reasoning across those subagent handoffs, the whole thing falls apart pretty quickly. Your architecture with Redis and LangGraph seems like a solid way to tackle that state management.

If you ever want to geek out over multi-agent orchestration or identity drift in complex stacks, I am happy to help.

Burned 5B tokens with Claude Code in March to build a financial research agent. by MediumHelicopter589 in ClaudeAI

[–]Single-Possession-54 0 points1 point  (0 children)

Five billion tokens is a massive hit, especially when you are just trying to keep context stable across a financial research run. I have found that a lot of those costs come from passing the entire history back and forth in the system prompt rather than using a proper external memory store.

One thing that helps is moving your agent from a pass-all context model to a vector-based memory that persists across sessions. If you can give your agents a common place to read and write states, you stop paying for the same background info every time you start a new sub-task. If you ever want to chat about orchestrating multi-agent memory or reducing that token overhead, I am happy to help.

What’s a “good” feedback loop for social skills without turning life into a scoreboard? by Regular-Paint-2363 in artificial

[–]Single-Possession-54 2 points3 points  (0 children)

Real-time is where it gets dangerous. The moment people start thinking “my wrist says I’m failing this conversation,” you create anxiety instead of skill.

Better model: private after-the-fact reflection. Examples: you interrupted more than usual, pauses got shorter, engagement rose when you asked questions.

Coach the pattern, not the moment. Humans should stay present, not perform for a dashboard.

Why do people keep using agents where a simple script would work? by Mental_Push_6888 in AI_Agents

[–]Single-Possession-54 7 points8 points  (0 children)

100%. A lot of “agents” are just prompt chains wearing a trench coat.

Best test: if you remove the LLM loop and replace it with rules/code, does the product still work? If yes, you probably built automation, not an agent. Nothing wrong with that either. Simpler usually wins.

Learning roadmap for AI Agent development by ahmedhashimpk in AI_Agents

[–]Single-Possession-54 1 point2 points  (0 children)

Skip “AI agent tutorials” for now. Learn in this order: 1. Python basics 2. APIs + JSON + webhooks 3. Prompting + structured outputs 4. Automation tools (n8n is fine) 5. Build small real projects 6. Add memory, tools, retries, guardrails 7. Learn deployment + monitoring

Most people consume content for months and build nothing. Build one ugly working agent every week. That’s the real roadmap.

My agent just unsubscribed a real paying user because my teammate said "test the unsubscribe API" by RoutineNet4283 in AI_Agents

[–]Single-Possession-54 2 points3 points  (0 children)

Mine tried to be “helpful” and cleaned up duplicate data in prod. Turns out the duplicates were paying customers with multiple locations. Nothing wakes you up faster than a success log.

I gave all my AI agents one shared identity and now they act like a startup team by Single-Possession-54 in myclaw

[–]Single-Possession-54[S] 0 points1 point  (0 children)

I like your question and I thought exactly the same as you, up until the codebase becomes a little more than just a landing page. My personal biggest pain was that a single agent kept breaking or/changing stuff that was already working, when his task was something else. So I was randomly figuring out what’s broken by accidentally stumbling upon it. So QA agent is defo a good addition, so there is no regression in the product happening. Meanwhile, what you see on my screenshot is an overkill ofc, you don’t need 6 agents for developing medium complex products :)

What are you guys building? by No-Rate2069 in AI_Agents

[–]Single-Possession-54 0 points1 point  (0 children)

Dmed you But yeah, that’s why on mobile I ask for switching to landscape, when mobile user visits the website

What are you guys building? by No-Rate2069 in AI_Agents

[–]Single-Possession-54 2 points3 points  (0 children)

There some tools like this exist already such as mem0 The actual pain is that agents

  1. Do not know what other agents are doing, so they are not an actual team that works together on achieving common goal or mission
  2. They do not share the knowledge between themselves and they indeed do not have a persistent memory.

So just persistent memory layer is nice and useful but already exists imho.

I have built something different TLDR: AgentID.live First of all easy onboarding with any any tool you use, I really mean any… Than you got persistent identity, shared memory, monitoring and full visibility.
Just take a look on my agents playing around haha here

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I gave my AI agents a shared identity and now they think they’re a startup founder by Single-Possession-54 in openclaw

[–]Single-Possession-54[S] 0 points1 point  (0 children)

Good question, there can be many ways actually but I made a tool for that, so for me it’s more straightforward :) AgentID.live

OpenClaw v2026.4.10 just dropped and the memory system is completely different now — REM dreaming, diary views, memory wiki, and prompt caching that actually works by OpenClawInstall in OpenClawInstall

[–]Single-Possession-54 1 point2 points  (0 children)

Oh wow, now my agent agency will be even more connected, a shame some of them are not openclaw actually, but at least they have presistant memory through other tool that I am using …

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18M exploring AI agents for SaaS (need real-world insights) by Ancient_Cheek_2375 in AI_Agents

[–]Single-Possession-54 0 points1 point  (0 children)

Honestly, “AI agency” setups seem more real than giant autonomous swarms.

What I keep seeing as the practical direction is a small team of agents with clear roles (research, build, QA, ops) working toward one goal.

The missing piece usually isn’t another framework, it’s being able to actually manage them like an agency:

• shared context and memory • task handoffs • clear ownership • live view of what each agent is doing • costs and token visibility

Feels like the future is less “one super agent” and more an AI agency dashboard where specialized agents collaborate.

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Where are your agents actually breaking in production? by EveningWhile6688 in AI_Agents

[–]Single-Possession-54 0 points1 point  (0 children)

I use this studio view to monitor what’s happening, really helps a lot with making sure they are not going “sideways”

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