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One connection, 2400+ tools, zero extra subscriptions. Try Monid with $1 in free credit: https://monid.ai?fpr=codist The same AI model can act like a basic chatbot in one app and a bug-fixing coding agent in another. The difference is the agent harness, and this video breaks down exactly how it works. We build an agent harness from the ground up by following a coding agent as it fixes a broken checkout flow. Along the way you'll see the agent loop (reason, act, observe, repeat), the orchestrator, context engineering, tools and MCP, sandboxes, verification, guardrails, memory and context compaction, and observability. By the end, you'll understand why agent = model + harness, and why harness engineering, not just better prompts, is what makes agents actually work. CHAPTERS 00:00 Same Model, Totally Different Behavior 01:32 Model vs Harness vs Agent 02:51 The Agent Loop: Reason, Act, Observe, Repeat 05:34 The Orchestrator & Task State 06:58 The Context Layer 08:04 Tools Are Not the Harness 09:20 Sponsor: Monid 10:52 Sandboxes & Processing Tool Output 12:50 Verification: Proving the Work Actually Works 14:05 Permissions & Guardrails 15:08 Memory vs Context 16:38 Long-Running Tasks: Compaction & Progress Files 18:37 Observability: Debugging an Agent 19:48 Prompt vs Context vs Harness Engineering + MCP 20:35 Coding vs Research vs Support Agents 21:28 Recap: How an Agent Harness Works SOURCES & FURTHER READING Anthropic, Effective harnesses for long-running agents: https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents Addy Osmani, Agent Harness Engineering: https://addyosmani.com/blog/agent-harness-engineering/ LangChain docs (Agent = Model + Harness): https://docs.langchain.com/oss/python/langchain/overview Model Context Protocol: https://modelcontextprotocol.io If this made agents click for you, subscribe for more AI explainers. Which part of the harness surprised you most? Tell me in the comments. #AIAgents #AgentHarness #HarnessEngineering

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