Harness Engineering for AI Agents: Build Production-Ready Agent Runtimes with Python, Context Engineering, MCP, Skills, Sandboxing, Evals, and Durable ExecutionEnglish | October 14, 2026 | ASIN: B0HMYY76VQ | 381 pages | Epub | 6.88 MB
Your AI agent can plan, call tools, and write code. But can it prove what it did, respect permissions, recover from a crash, and stop before a costly mistake?That is the difference between an impressive AI demo and a dependable agent runtime.Harness Engineering for AI Agents is a hands-on, Python-first guide to building the control systems behind reliable agentic AI. Go beyond prompts and agent loops to design an AI agent harness that governs tools, protects data, preserves state, measures outcomes, and produces evidence you can trust.Instead of disconnected tutorials, you will build HarnessLab-one evolving project that starts with a deliberately broken software repository and grows into a controlled, testable agent runtime. Each major milestone follows a practical BUILD → INSPECT → BREAK → REPAIR → PROVE workflow, so you learn not just how a system works, but how it fails and how to verify the repair.Inside, you will learn to:Build Python AI agents with bounded ReAct loops, model adapters, structured responses, typed tools, and execution budgets.Engineer agent context and memory using repository retrieval, persistent SQLite records, state management, and reusable agent skills.Integrate Model Context Protocol (MCP) tools, resources, prompts, and MCP servers while keeping capability boundaries explicit.Secure AI agent execution with default-deny policies, human approvals, sandboxing, scoped file access, and prompt-injection defenses.Make long-running agents resilient with durable checkpoints, safe retries, idempotency, task queues, worker leases, and crash recovery.Evaluate and observe agent behavior through regression tests, trajectory evaluations, structured logs, traces, latency metrics, token usage, and USD cost budgets.Prepare agent runtimes for deployment with a typed API, reproducible configuration, operational checklists, backup drills, and rollback procedures.One harness. Two verified capstone workflows.First, guide a coding agent from an issue report to a reviewed software patch, interrupt execution, resume safely, and verify the result. Then reuse the same runtime to triage synthetic support tickets without sending unauthorized messages. Both workflows emphasize approvals, observable actions, and traceable evidence.Written for Python developers, AI engineers, software architects, and technical practitioners, this book assumes basic Python and command-line familiarity-not prior expertise in agent frameworks. It includes 27 chapters, six practical appendices, complete source listings, fixtures, test cases, and copy-ready contracts. The core exercises work offline without a required GitHub repository or paid model API.Stop treating a confident agent response as proof. Build AI agent systems that can show what happened, why it was allowed, and whether the outcome was correct.
