Learn Before
Failure-Driven Harness Optimization and Procedural Memory - Long-Horizon Agent Reliability: Stateful Scaffolding and Runtime Verification @ University of Michigan - Ann Arbor
Harness Scaling Foundations and Execution Failure Gaps - Autonomous Agent Control Planes: State Scaffolding and Resilient Execution @ University of Michigan - Ann Arbor
Failure-Driven Harness Optimization
Failure-driven harness optimization is an iterative development process that systematically converts postmortem findings from agent execution failures into versioned runtime controls without modifying underlying model weights. Following a failed run, defects are categorized into operational causes—such as missing context, invalid transitions, weak verification checks, premature handoffs, or ineffective recovery. The executing agent or a supervisory hyper-agent then proposes targeted modifications to state boundaries, prompts, entry/exit hooks, pre-commit checks, recovery rules, and practice activation criteria. Proposed changes are reviewed, regression-tested against existing tasks, and merged into a new versioned runbook.
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Prep Sessions
Long-Horizon Agent Reliability: Stateful Scaffolding and Runtime Verification @ University of Michigan - Ann Arbor
Ch.3 State Persistence and Continuous Optimization - Long-Horizon Agent Reliability: Stateful Scaffolding and Runtime Verification @ University of Michigan - Ann Arbor
Failure-Driven Harness Optimization and Procedural Memory - Long-Horizon Agent Reliability: Stateful Scaffolding and Runtime Verification @ University of Michigan - Ann Arbor
Autonomous Agent Control Planes: State Scaffolding and Resilient Execution @ University of Michigan - Ann Arbor
Ch.1 Execution Lifecycle and State Boundaries - Autonomous Agent Control Planes: State Scaffolding and Resilient Execution @ University of Michigan - Ann Arbor
Harness Scaling Foundations and Execution Failure Gaps - Autonomous Agent Control Planes: State Scaffolding and Resilient Execution @ University of Michigan - Ann Arbor
Related
Failure-Driven Harness Optimization
Golden Rules for Harness Profile Development
Control Drift in Multi-Task Harness Optimization
Sparse Routing in Shared Agent Runbooks
Multi-Task Harness Abstraction via Hyper-Agent
Procedural Practices in Agent Harnesses
Single-Task Harness Adaptation Loop
Selective Filtering in Harness Learning
Procedural-Memory Gap in Autonomous Agents
Sources of Execution Failure in Long-Horizon Agents
Control-Signal Dilution
Harness Scaling
Mutable-State Ambiguity
Three Empirical Tests of Harness Scaling
Failure-Driven Harness Optimization
Learn After
Which entities are responsible for proposing targeted modifications to runtime controls after an execution failure?
True or False: Proposed harness modifications take effect immediately in production without requiring review or regression testing against existing tasks.
In failure-driven harness optimization, how are postmortem findings applied to prevent future agent execution failures without retraining or modifying underlying model weights?
Golden Rules for Harness Profile Development
Control Drift in Multi-Task Harness Optimization
Multi-Task Harness Abstraction via Hyper-Agent
Explain the role of operational defect categorization in failure-driven harness optimization and describe three specific operational causes identified during postmortems.
Match each operational failure cause to the execution defect it describes.
Failure-driven harness optimization is a(n) ___ development process that systematically converts postmortem findings from execution failures into versioned runtime controls.
Order the steps of the failure-driven harness optimization cycle following an agent execution failure.