Context Engineering and Harness Engineering: Building Reliable AI Agents Beyond Prompts

Prompt engineering tells the model what to do. Context engineering gives it the right information. Harness engineering builds the system that helps it act, verify, and recover. When developers first started building applications with LLMs, much of the work revolved around prompts: improve the instructions, add a few examples, adjust wording, and hope the model behaves better. That approach works surprisingly well for simple tasks. But consider a coding agent asked to: Add rate limiting to an existing Node.js API without breaking authentication. A useful agent needs much more than a carefully written prompt. It may need to understand the repository architecture, inspect authentication middleware, read engineering conventions, modify files, run tests, execute ESLint and TypeScript, inspect failures, correct its implementation, and possibly ask for human approval before changing sensitive infrastructure. This is where context engineering and harness engineering become important. They solve related-but different-problems. From Prompt Engineering to Context Engineering Prompt engineering primarily deals with how instructions are expressed. Context engineering asks a broader question: What information should the model have available at this particular moment? Anthropic describes context engineering as curating and maintaining the optimal information supplied to an LLM during inference. That context can include far more than the system prompt: conversation history, retrieved documents, tools, MCP resources, previous tool results, memory, application state, and external data. (Anthropic) Think of an agent's context as its working memory. For our Node.js example, the model might receive: Task ├── Add API rate limiting │ Context ├── AGENTS.md ├── architecture.md ├── package.json ├── auth.middleware.ts ├── existing API conventions ├── relevant test files └── previous tool results The difficult problem isn't simply retrieving information. It is deciding what deserves to enter the context window. Dumping an entire repository into the model is rarely ideal. More context does not automatically produce better reasoning. Anthropic notes that model performance can degrade as increasingly large amounts of information compete for attention, making context a finite resource that should be carefully managed. (Anthropic) A good context-engineering system therefore tries to maximize signal rather than volume. Instead of: Load entire repository → send 200,000 tokens → ask model to figure everything out it might do: Understand task ↓ Search repository ↓ Retrieve relevant modules ↓ Retrieve engineering rules ↓ Add recent tool results ↓ Construct focused context ↓ Model This is why techniques such as RAG, memory, repository search, context compression, tool-result filtering, and just-in-time retrieval are fundamentally context-engineering techniques. Harness Engineering Goes One Layer Further Providing excellent context still doesn't make an LLM a reliable software agent. The model needs an environment around it. One increasingly common mental model is: Agent = Model + Harness LangChain describes a harness broadly as the code, configuration, tools, infrastructure, state and orchestration surrounding the model. (LangChain) A simplified architecture looks like this: ┌─────────────────────┐ │ Harness │ │ │ User ──────────►│ Context Management │ │ Tool Execution │ │ Memory / State │ │ Permissions │ │ Validation │ │ Retry / Recovery │ │ Observability │ │ │ │ │ ▼ │ │ ┌─────────┐ │ │ │ Model │ │ │ └─────────┘ │ └─────────────────────┘ The model provides intelligence. The harness determines how that intelligence interacts with the real system. For example, the model may decide: "I should run the tests." But something outside the model must actually: - expose the testing tool, - execute the command, - capture stdout and stderr, - enforce timeouts, - return relevant output, - prevent unsafe commands, - let the agent decide what to do next. That machinery belongs to the harness. The Most Important Difference: Information vs Control A useful way to separate these concepts is: | Context Engineering | Harness Engineering | | |---|---|---| | Main question | What should the model know now? | How should the agent operate? | | Focus | Information | Execution environment | | Examples | RAG, memory, instructions, retrieved files | tools, sandboxes, retries, validation, permissions | | Main constraint | Limited model attention | Unreliable agent actions | | Goal | Better reasoning | Reliable execution | They overlap heavily. In fact, Martin Fowler's discussion of coding-agent harnesses describes context engineering as one of the mechanisms through which guides and feedback can be made available to agents. (Martin Fowler) So it is better to think of these ideas as layers rather than competing approaches. Prompt Engineering ↓ How should I instruct the model? Context Engineering ↓ What should the model know? Harness Engineering ↓ How should the complete agent system operate? A Realistic Coding-Agent Harness Imagine our agent has implemented rate limiting. A weak system might stop as soon as the model says: Done. A production-oriented harness shouldn't trust that statement. Instead: Agent modifies code ↓ TypeScript compiler ↓ ESLint ↓ Unit tests ↓ Integration tests ↓ Architecture checks ↓ Security checks ↓ Agent receives failures ↓ Self-correction ↓ Human review This introduces something extremely important to AI engineering: deterministic verification around probabilistic intelligence. If TypeScript reports: TS2345: Argument of type 'undefined' is not assignable to parameter of type 'string' we don't need another LLM to decide whether compilation succeeded. The compiler already knows. Likewise, established software engineering tools-tests, linters, type checkers and structural analysis-can act as fast deterministic feedback mechanisms around an agent. Fowler separates such computational controls from inferential checks such as AI-based code review. (Martin Fowler) The strongest harnesses combine both. Guides and Sensors Another useful harness model is guides and sensors. Guides influence the agent before it acts: AGENTS.md architecture.md coding standards API documentation security policies examples skills Sensors tell the agent what happened after it acted: compiler errors test failures lint warnings runtime logs browser results security scanners AI code reviews The loop becomes: Guides │ ▼ Agent │ ▼ Action │ ▼ Sensors │ └────────► Agent corrects itself Fowler argues that combining these feed-forward guides with feedback sensors can improve both first-attempt quality and self-correction. (Martin Fowler) This is much stronger than continually expanding a system prompt with another ten paragraphs of rules. Harness Engineering Doesn't Replace Context Engineering A common mistake is to interpret the evolution as: Prompt engineering ↓ Context engineering ↓ Harness engineering as though each one makes the previous technique obsolete. They actually operate at different scopes. A real agent may use all three simultaneously. For example: Prompt engineering Follow the repository's existing architecture. Context engineering Retrieve: architecture.md existing controller existing service relevant tests Harness engineering Provide: filesystem repository search terminal test runner linting sandbox permissions git diff observability retry loop The harness can even continuously improve the context available to the model. That relationship is why harness engineering is becoming important as agents move from answering questions toward performing long-running software-engineering work. What Changes for Software Engineers? The interesting shift is that AI engineering is becoming less about finding a magical prompt and more about system design. The important questions increasingly look familiar: What information does this component need? What capabilities should it have? What actions are permitted? How do we know the result is correct? What happens when something fails? Can the operation be retried safely? How do we observe what happened? Where should a human approve the action? Those aren't really prompting questions. They're software-engineering questions. And that may be the most useful way to understand the relationship between these concepts: Context engineering improves the model's view of the world. Harness engineering engineers the world in which the model operates. As agents become more capable, the quality of the model will certainly matter. But increasingly, the reliability of an AI application will depend on everything surrounding that model: context selection, tools, state, permissions, deterministic validation, feedback loops and observability. The model may be the intelligence. The surrounding engineering is what turns that intelligence into a dependable system. Further Reading Anthropic's Effective context engineering for AI agents provides a strong practical explanation of context selection and attention constraints. (Anthropic) Martin Fowler's Harness engineering for coding agent users develops the useful guides-and-sensors model. (Martin Fowler) LangChain's The Anatomy of an Agent Harness explores the broader Agent = Model + Harness interpretation. (LangChain) Top comments (0)