Harness & Orchestration Engineering for AI
Design the control systems and multi-agent orchestration that make autonomous AI agents dependable in production.
About this course
Two disciplines make production AI agents dependable: harness engineering, which governs how a single agent perceives its environment, selects actions, and validates outputs — and orchestration, which coordinates many of those agents, deciding who does what, when, sequentially or in parallel. This course teaches both together, not as an add-on. It opens by contrasting the two head-on, then works through the four functions every harness performs — constrain, inform, verify, correct — and the layered architecture behind them. Orchestration patterns — orchestrator-worker coordination, sequential vs. parallel execution, routing and delegation — arrive mid-course, once the harness fundamentals are in place, so the guardrails, verification pipelines, and feedback loops that follow are taught with both a single agent and a coordinated multi-agent system in mind. It closes with a capstone unifying the two into nested harnesses around every subagent, and two real production case studies: a 1M+ line codebase built almost entirely through a harness, and a multi-agent research system where orchestration and harnessing work together. Every module includes interactive diagrams, flowcharts, and data visualizations built to make the concepts concrete.
Curriculum (8 modules)
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What Is Harness Engineering & Orchestration
The core definitions of both disciplines, how a harness differs from an orchestrator, how both differ from prompt/context engineering, and the production data behind why they matter.
The Four Functions of a Harness
Constrain, inform, verify, correct — the continuous cycle every reliable agent harness runs, and how the same cycle reappears one level up in an orchestrator.
Harness Architecture
The five-layer architecture — guides, sensors, context pipelines, boundaries, and feedback — how a task flows through it, and how it wraps every agent in a multi-agent system.
Orchestration Patterns
Orchestrator-worker coordination, sequential vs. parallel execution, routing and delegation, and how Anthropic's own multi-agent research system uses them in production.
Guardrails & Verification Pipelines
Turning constraints into enforced tooling — architectural boundaries, CI verification gates, guardrail strength levels, and verifying merged multi-agent output.
Feedback Loops & Self-Repair
Detecting drift, building bounded self-repair loops, designing escalation paths to humans, and retrying just the failed branch of a delegated task.
Unifying Harness & Orchestrator
Why the orchestrator needs its own harness, how nested harnesses wrap every subagent, and the full system diagram tying both disciplines together.
Case Study & Production Patterns
Two real production systems — a 1M+ line codebase built with zero human-written code, and a multi-agent research system — plus best practices and pitfalls across both disciplines.
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