Harness Engineering: What's Actually New (And What Isn't)
Summary
Harness engineering refers to the entire infrastructure surrounding Large Language Models (LLMs)—including tools, context management, guardrails, and telemetry—that ensures agents operate correctly. While the non-deterministic nature of LLMs introduces novel challenges (like model drift and complex evaluation), the core principles of software engineering (security, observability, incremental development, and testing) remain critical fundamentals for building robust AI agents.
Key takeaways
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Harness Engineering Components
5:26
The agent system comprises the LLM (reasoning engine) and the harness, which includes context (RAG, system prompts), tools (APIs), orchestration, guardrails, security, and telemetry. These elements build upon prompt, context, and loop engineering practices.
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Non-Deterministic Challenges
21:30
Unlike deterministic software, LLMs are subject to non-determinism, model drift (e.g., changes in phrasing without version updates), and the LLM itself decides whether to invoke a tool, requiring robust telemetry and guardrails to detect failures.
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Evolution of Testing
28:50
Traditional Test-Driven Development (TDD) evolves into AI agent evaluations. Due to non-deterministic outputs, dedicated evaluation scorers (using a separate 'judge' model) are necessary to validate quality and relevance, and these must be integrated into the CI/CD pipeline.
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The Persistence of Fundamentals
33:50
Core software engineering best practices—such as trunk-based development, small incremental changes, continuous testing, security (OWASP principles), and observability (logs, metrics, traces)—are not obsolete and must be applied to agentic systems.
Technical details
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Agent Architecture & Context
445s
The context window is expanded by incorporating Retrieval Augmented Generation (RAG) from vector databases (e.g., Elasticsearch), system prompts, and external skills/tools. Data discovery remains crucial, as LLMs can hallucinate due to training cutoffs, biases, or over/under-fitting.
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Tool Definition and OpenAPI
1741s
Tools (capabilities) must be defined using specifications like OpenAPI or Zod TypeScript schemas. The description provided to the LLM must be highly engineered to ensure the model correctly decides when and how to invoke the tool.
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Observability and Guardrails
1920s
Implementing telemetry (logs, metrics, traces) is vital for diagnosing agent behavior, especially non-deterministic actions. Guardrails must be in place not only to restrict actions (e.g., topic restriction) but also to be monitored via telemetry to detect when the agent attempts to bypass them.
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Security and Authorization
2010s
Security principles like the principle of least privilege, data-level entitlements, and authentication/authorization remain paramount. The OWASP Top 10 list has evolved to include specific vulnerabilities for GenAI applications, such as prompt injection.
Mentioned resources
Channel & topics
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