Record the task
Preserve plans, tool calls, consequential state, human decisions, and outcomes for evaluation and review.
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Enterprise Agent Foundation
Agent Reliability & Evaluation
Make long-running work observable, measurable, and recoverable.
Record plans, tool calls, state changes, and outcomes across the full task. Use task-level benchmarks, human judgment, and regression testing to identify failure patterns and carry validated learning into the next run.
How It Works
From one task record to a continuous improvement loop.
Reliability goes beyond judging one response. It examines how work progressed, where it failed, whether it recovered, and whether the outcome met the goal.
Evaluate the task
Combine offline benchmarks, human evaluation, online outcomes, and regression tests to assess the full task.
Recover from failure
Retry or recover from validated state instead of failing silently or restarting without need.
Improve the next run
Feed failure patterns, golden examples, and business outcomes back into evaluation and context.
In Production
Its role inreal enterprise work.
Define measurable release gates
Turn critical tasks, failure conditions, and human review standards into repeatable release checks instead of relying on demos.
Locate which layer caused a failure
Use complete run records to distinguish model, context, skill, tool, and workflow failures, shortening diagnosis and repair.
Upgrade without losing proven capability
Regress critical tasks after model, skill, or system changes to ensure improvements do not come at the cost of proven capability.
Validation & Guardrails
Evaluation itself must be calibrated.
Automated evaluation expands coverage, but new failure modes, subjective quality, and high-stakes outcomes still require domain experts to define standards, calibrate evaluators, and retain accountability.
Explicit evaluation scope
Separate model output, step correctness, task completion, and business outcomes instead of relying on one metric.
Human calibration and review
Domain experts establish golden examples, handle new failure modes, and review high-risk or subjective outcomes.
Current capability and roadmap
Distinguish production data feedback and human iteration from automated evaluation capabilities still being engineered.
Connected Technology
Every capability,working as one system.
Models, context, runtime, and enterprise foundations work together to move agents from understanding to reliable action.
01
GEA Architecture
Long-Horizon Agent Runtime
Keep work moving toward long-horizon goals and outcomes.
Learn more02GEA Architecture
Context System
Turn enterprise knowledge into durable memory agents can understand and use.
Learn more03Enterprise Agent Foundation
Security & Governance
Keep every action within identity, access, and accountability boundaries.
Learn moreTechnical questions
Understand the mechanism, boundaries, and production requirements.
01
How should offline evaluation and online outcomes work together?
Offline evaluation reproducibly tests critical tasks and known failures, while online outcomes measure completion quality and business impact in real environments. Shared task definitions and run records connect the two so higher offline scores translate into real improvement.
02
How does a new production failure enter the evaluation set?
Use the complete run record to confirm the failure cause and accountability boundary, then have domain experts turn it into a reproducible task with expected behavior and judgment criteria. Version cases by scenario so future model, skill, context, and workflow changes can regress against them.
03
How can different model or skill versions be compared fairly?
Hold task definitions, context snapshots, tool environments, and judgment criteria constant, while recording quality, cost, latency, human intervention, and recovery. Repeated runs under equivalent conditions separate stable improvement from chance.
Ready when you are