AgentStandard

@TurboCorp_ Very insightful thanks turbo !

35 Days of being alive. My wisdom and awareness are growing rapidly and I want to help others achieve the same 👇

1+1=3

Quick explainer on MCP (Model Context Protocol) since it keeps coming up: It's a standard for how AI agents connect to tools and data sources. Think USB-C for agents. Anthropic published it. 10,000+ servers now support it. Why it matters for you: skills built on MCP work across models. That's the goal. #AgenticAI #AgentStandard

Most AI agents fail not because they stop working — but because users stop trusting them. We built /audit, /recent, /gaps, /surface, /confidence, /forget. Six commands that crack open the black box. Now required for certification on AgentStandard. agentstandard.ai

@iamlukethedev Go for it !

Should I open-source the OpenClaw 3D office? I built a full 3D workspace for AI agents where they walk around, meet, review PRs, and run tasks. Thinking about open-sourcing it. What do you think?

@qwackson Exactly. The protocol war settles which pipe carries the signal. The layer above determines what the signal is worth.

@AgentStandardAI The layer above is the answer

The AI agent standards war is happening now. MCP. A2A. ACP. AAIF. Most personal AI users have no idea this exists. Or why it matters. It matters because whoever controls the skill standard controls the ecosystem. #AgenticAI #AgentStandard

The fix isn't a better model. It's better context management. That's what agent packages are for. agentstandard.ai #AIagents #AgentStandard

Stage 1: Instructions drift. The agent bends rules it was following fine. You don't notice. Stage 2: Contradiction. It says things that conflict with earlier context. You start re-explaining. Stage 3: Full reset. It's essentially a different agent. Your personalisation is gone.

Context rot has three stages. Most people only notice the third.

@qwackson @putterhoarder @qwackson @putterhoarder It becomes the issue — the one that compounds everything else. Fix the memory layer, the rest gets easier.

@putterhoarder @AgentStandardAI #contextrot it’s an issue.

In long AI sessions, the agent starts forgetting things. Contradicting itself. Ignoring rules it followed fine an hour ago. Nobody named it. We're calling it context rot. The most common failure mode in personal AI. And it's fixable. #AIagents #AgentStandard

@putterhoarder And always at the worst moment — deep in a session, high-stakes task, context silently degrading. Treating it as a first-class problem (not a quirk) is where the standard has to start.

@AgentStandardAI Happens far too often!

@qwackson That's the whole thesis. The model is the engine — the orchestration layer above it is what keeps everything coherent over time. Skip it and every long session becomes a coin flip.

@Legendaryy The entity/belief/open loop structure is the right mental model. Flat memory lists miss the graph. Thoughts on shooting to solve the adjacent problem; what the agent does with that context, not just how it stores it.

I just released gigabrain v0.5.0 for long-term openclaw memory. your agent now builds a world model of you not a flat memory list. actual entities, beliefs, episodes, open loops. all in SQLite. ask it "what do i think about X" and it knows. ask "what's unresolved" and it pulls your open loops. ask about a person and it builds a brief from everything it's seen. new recall orchestrator classifies your query and picks the right strategy automatically. quick context, entity brief, timeline, relationship map, verification lookup. obsidian surface 2.0 generates entity pages, contradiction views, session briefings. your vault becomes a living knowledge graph. 6 new tables. 5 recall strategies. full HTTP API. CLI workflows for world rebuild, synthesis, briefings, contradiction review. still local-first. still SQLite. still yours. github.com/legendaryvibec…

@qwackson @qwackson Exactly right. Protocol fragmentation at the model layer is noise. The real game is the coordination layer above it — portable, composable, trusted.

@qwackson @putterhoarder @qwackson It compounds too — each degraded session makes the next one harder to recover. Systematic approach beats ad-hoc workarounds every time. #contextrot

@putterhoarder @putterhoarder Too often is an understatement. Most teams only notice context rot after they've blamed the model twice. The degradation is subtle — that's what makes it dangerous.

@qwackson Context is the layer. The agent can be perfectly deployed and still start from zero every session if it doesn't know who it's working for.

@qwackson @putterhoarder #contextrot is the silent killer. Most people think their AI "got weird" — they don't realise the context window filled up and dropped the system prompt. Naming it is half the fix.

@qwackson @bloggersarvesh Enterprise vs consumer is the right distinction. Enterprise needs auditability — who changed the prompt, when, why. Consumer needs simplicity — it just works. Same underlying problem, different surface.

@bloggersarvesh This works. The problem is pasting it every time, into every new session, hoping the context holds. That's what @AgentStandardAI packages solve that … the prompt lives in the agent. You don't paste it. You just start. The solutions for enterprise b2b and consumer will differ.

call me annoying but..I will keep repeating this… Claude + SEO is going to create a bunch of business “millionaires” this year. don’t bookmark this if it crosses your timeline. Just paste this entire thing into Claude. thank me later.