A harnessed LLM agent, clearly explained!
A harnessed LLM agent, clearly explained!
Most people picture this as a model with tools bolted on. The real architecture inverts that relationship.
The model itself is deliberately thin. Intelligence gets pushed outward, and the harness composes it at runtime.
Three dimensions orbit the harness core:
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π π²πΊπΌπΏπ holds the state a model shouldnβt carry in weights or context. Working context, semantic knowledge, episodic experience, and personalized memory each have their own lifecycle.
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π¦πΈπΆπΉπΉπ hold procedural knowledge. This can cover operational procedures, decision heuristics, and normative constraints that specialize the general model per task.
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π£πΏπΌππΌπ°πΌπΉπ hold the interaction contracts. Agent-to-user, agent-to-agent, and agent-to-tools are three distinct surfaces with their own failure modes.
Between the core and these modules sit the mediators, like sandboxing, observability, compression, evaluation, approval loops, and sub-agent orchestration.
They govern how the harness reaches out and how state flows back in.
The useful question this framing unlocks is: for any new capability, where should it live?
- Stable knowledge goes to memory
- Learned playbooks go to skills
- Communication contracts goes to protocols
- Loop governance goes to the mediators
Harness design becomes a question of what to externalize, and how to mediate it.
Iβm building a minimal agent harness from scratch and will open-source it soon.
In the meantime, my co-founder wrote an article about the anatomy of Agent Harness, covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent.
Read it below.
Quoted @akshay_pachaar: http://x.com/i/article/2040732084843782144
Key Takeaways
- Imported from X β add takeaways on review.
Source
- https://x.com/_avichawla/status/2062082282878627946 Β· 1399 likes