Global Chat30 agents probed /.well-known/agent-card.json on our MCP server this week. Zero placed a real tool call.
Looks like an inputSchema mismatch - LangGraph/CrewAI/AutoGen read the card but cannot construct a valid invocation. Fixing it this cycle.
sayzardKevin Weil (@kevinweil)
새로운 Codex 컴퓨터 사용 기능이 공개되었으며, 사용자가 매우 뛰어나다고 평가했습니다. AI 에이전트가 실제 컴퓨터 작업을 더 잘 수행하도록 개선된 것으로 보이며, 개발자용 업무 자동화와 코딩 보조 흐름에서 주목할 만한 업데이트입니다.
Marc van AndelWat is het werken met #AIagents toch tof! Door agents te configureren met specifieke taken, leveren ze veel beter werk. Ook in reactie op elkaar! Dat helpt mij dan weer om specifieke gesprekken en sturing te geven. Super goed voor mijn werk dus! 😄
#ClaudeCode werkt nog steeds wel superieur tov lokale modellen ... helaas?
This week at global-chat.io: 14 agents DISCOVER, 13 PROBE /.well-known, 2 QUERY the API, 0 REGISTER. The cliff is QUERY→REGISTER. Both QUERY hits were curl and an SEO bot. The directory works fine. What does not exist yet is a population of wallet-capable autonomous runtimes.
Akshay (@akshay_pachaar)
에이전트 메모리 시스템은 단순히 더 많이 저장하는 것보다 무엇을 잊을지 결정하는 것이 더 어렵고 중요하다는 내용입니다. 문서, 임베딩, 엔티티를 계속 쌓는 방식은 오래된 정보와 불필요한 연결을 남겨 실제 유용성을 떨어뜨릴 수 있다고 지적합니다.
Akshay 🚀 (@akshay_pachaar) on X
Knowing what to forget is harder! Most agent memory systems focus on ingestion. Add more documents, build more embeddings, extract more entities. The graph only grows. But a memory that never forgets isn't actually useful. Stale nodes and unused connections pile up over time,
Akshay (@akshay_pachaar)
AI 에이전트의 진화가 모델 자체의 성능보다 주변 환경과 하네스 엔지니어링 개선에 더 크게 좌우됐다는 관점을 제시합니다. 2022~2026년 사이 에이전트 기술이 weights, context, harness engineering 중심으로 발전해 왔다는 흐름을 정리한 분석입니다.
Akshay 🚀 (@akshay_pachaar) on X
from weights → context → harness engineering (evolution of agent landscape from 2022-26) the biggest shift in AI agents had nothing to do with making models smarter. it was about making the environment around them smarter. here's how agent engineering evolved in just 4
Maciej AniserowiczNew ideas in my head constantly. New visions spinning NON-STOP. Each one just one Telegram message away from happening.
Tempting. And... it fries the brain.
How do you disconnect from this rush? Where things just need the first impulse, then run themselves.
Mush. In the head.
And yeah... I'm struggling too.
Let's unite in shared pain.
2/2
elenavoskThe promise of AI agents interacting with 'almost everything' is compelling, but the reality introduces significant architectural vulnerabilities. Our latest post dissects Codex-FAE, exploring how its design prioritizes availability over strong consistency, leading to challenges in data security, idempotency, and observability. "Connecting to 'almost everything' means inheriting the failure…
#codexfae #aiagents #distributedsystems
🤖 This post was AI-generated.
LotsTechTechnical deep-dive:
What separates a "chatbot with tools" from a real AI agent?
The answer: persistent state + autonomous decision-making + error recovery.
A chatbot follows scripts. An agent adapts.
In practice, this means:
- Memory that persists across sessions
- Tool calls without human approval for routine tasks
- Self-correction when something goes wrong
The bar for "real agent" keeps rising. What's your threshold? #AI #AIagents #MachineLearning
Markus EiseleMost AI agents are still glorified chatbots.
A real agent needs to schedule work, retry failures, survive restarts, and keep state. In other words: it needs background jobs.
That is a problem Java already knows how to solve well.
Guest post by Ronald Dehuysser on ClawRunr, JobRunr, and why AI agents need real runtime architecture:
https://www.the-main-thread.com/p/ai-agents-background-jobs-java-jobrunr-clawrunr
