--- title: "A harnessed LLM agent, clearly explained!" url: https://stacklist.com/card/e5f0cd28-e158-4142-8df1-835a0e851556 source_url: "https://x.com/_avichawla/status/2062082282878627946" stack: https://stacklist.com/stack/539051c9-e760-4bd5-a86b-ac120e4ae368 summary: "A harnessed LLM agent architecture is explained, where intelligence is pushed outward from a deliberately thin model into three core modules: Memory, Skills, and Protocols, mediated by components like sandboxing, observability, and sub-agent orchestration. The post outlines a design framework for deciding where new capabilities should live and references an upcoming open-source minimal agent harness along with a companion article on agent anatomy." tags: "llm-agents, agent-harness, orchestration, memory, ai-architecture, agentic-design, context-management" key_entities: "Avi Chawla (person), Akshay Pachaar (person), Agent Harness (concept), LLM Agent Architecture (concept), Anthropic (organization), OpenAI (organization), LangChain (organization), Perplexity (organization), Orchestration Loop (concept), Memory Management (concept)" classification: "framework" content_hash: "sha256:d1dd443680ebfa9a23f887ff8ef33f8298e1cad7c4e34e357aa6b7163166b1db" acp_version: "0.2" token_counts_approximate: 1050 visibility: public agent_accessible: true status: "final" --- # A harnessed LLM agent, clearly explained! Avi Chawla @_avichawla 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 rness core: - π— π—²π—Ίπ—Όπ—Ώπ˜† holds the state a model shouldn't carry in weights or context. Working context, semantic knowledge, episodic experience, and personalized memory each have their own lifecycle. - π—¦π—Έπ—Άπ—Ήπ—Ήπ˜€ hold procedural knowledge. This can cover operational procedures, decision heuristics, and normative constraints that specialize the general model per task. - π—£π—Ώπ—Όπ˜π—Όπ—°π—Όπ—Ήπ˜€ 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 go to protocols - Loop governance goes to the mediators Harness design becomes a question of what to externalize, and how to mediate it. I'm 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. GIF Akshay πŸš€ @akshay_pachaar Apr 6 Article The Anatomy of an Agent Harness A deep dive into what Anthropic, OpenAI, Perplexity and LangChain are actually building. Covering the orchestration loop, tools, memory, context management, and everything else that transforms a... 8:01 AM Β· Jun 3, 2026 211.2K Views :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 3 8 number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 38 :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 2 3 3 number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 233 :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 1 . 4 K number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 1.4K :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 2 . 1 K number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 2.1K Read 38 replies