Multi-Task Harness Abstraction via Hyper-Agent
Multi-task harness abstraction is a two-tiered optimization workflow designed to synthesize durable procedural runbooks across broad task families:
- Task-Level Execution & Proposal: Individual task agents execute training instances and propose localized procedural changes directly from their observed failure traces.
- Hyper-Agent Evaluation: A more capable supervisory hyper-agent evaluates candidate proposals across multiple instances, evaluating their generality, safety, and cross-task compatibility.
- Reconciliation & Freezing: The hyper-agent reconciles validated lessons into a shared family-level runbook and removes idiosyncratic task workarounds before freezing the profile for held-out evaluation.
This division of labor prevents singular instance failures from triggering over-specialized family rules while automating the synthesis of persistent procedural memory.
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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
Related
Failure-Driven Harness Optimization
Golden Rules for Harness Profile Development
Control Drift in Multi-Task Harness Optimization
Sparse Routing in Shared Agent Runbooks
Procedural Practices in Agent Harnesses
Single-Task Harness Adaptation Loop
Selective Filtering in Harness Learning
Procedural-Memory Gap in Autonomous Agents
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?
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.
Learn After
In the multi-task harness abstraction workflow, what is the primary responsibility of the supervisory hyper-agent during the evaluation phase?
In multi-task harness abstraction, individual task agents freeze the shared family-level runbook before submitting their failure traces to the hyper-agent.
Why does multi-task harness abstraction employ a two-tiered division of labor between task agents and a supervisory hyper-agent rather than allowing task agents to update the family runbook directly?