Harness Engineering: Building the Production Cage for Powerful Domain Agents — Mike Chambers, AWS
The presentation introduces 'Harness Engineering,' a critical concept for building production-grade AI agents at scale. Mike Chambers distinguishes between agents that are used (e.g., coding assistants) and agents that are built. For built agents, the harness encompasses all non-model components—such as memory, skills, tools, identity, and context management—that must scale independently. The core principle is that scaling these components separately, rather than deploying them in a single container, is essential for handling thousands of users and maintaining reliability.
Key takeaways
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Two Types of Agents
4:05
Agents are categorized into 'agents we use' (productivity tools, coding assistants) and 'agents we build' (production-scale systems). The approach for built agents requires careful architectural planning.
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Defining the Harness
7:04
A harness is defined by subtraction: take an agent and remove the model component; everything left over is the harness. This includes the infrastructure, skills, and tools.
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Scaling Built Agents
10:57
For production agents, the harness must manage complex concerns like loop management, scaling, payments, identity, runtime, context management, and observability. Attempting to containerize everything together is incorrect for high scale.
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Avoiding 'Slop Ops'
10:07
Build engineers must avoid 'slop ops' (clicking around a console to deploy resources). Instead, agents must build infrastructure using Infrastructure as Code (IaC) to maintain ownership and control over cloud deployments.
