Capture the execution trajectory
Preserve plans, context, model and tool calls, state, human judgment, and outcomes as evidence of the complete run.
Back to Technology
Production Readiness
Agent Reliability & Evaluation
Make every run observable and measurable—and improve the next one.
Capture the full execution path and return failures found through production observability to evaluation sets, evaluators, and regression gates—closing the loop from build to run to observe to improve so every upgrade can be verified.
How It Works
Turn failures from real runs into a repeatable improvement loop.
Evaluation dimensions should not come only from predefined benchmarks. Complete execution trajectories surface new failures, which domain experts turn into criteria and use to calibrate automated evaluators.
Derive a failure taxonomy
Find anomalies in real runs, group them into failure dimensions, and have domain experts define criteria and golden examples.
Calibrate evaluators
Align automated evaluators to human-labeled golden examples and retain human review for novel or subjective failures.
Enforce regression gates
Rerun critical tasks after changes to models, context, skills, or systems, promoting only improvements that pass regression gates.
Enterprise Value
Turn one-off success into repeatable capability.
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 execution trajectories to distinguish model, context, Agent Harness, 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 continuously calibrated.
Automated evaluation expands coverage, but new failure modes, subjective quality, and high-stakes outcomes still require experts to define standards, calibrate evaluators, and retain accountability. Business outcomes validate whether evaluation reflects value rather than replacing professional judgment.
How We Measure
01
Task-level success
02
Evaluator-human agreement
03
Critical-task regression pass rate
04
Failure recovery success
Boundaries & Guardrails
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.
01
GEA OS · Agent Development & Runtime
Agent Development & Runtime
Build and orchestrate agents so they can keep working in real operations.
Learn more02GEA OS · Context Organization & Evolution
Context Organization & Evolution
Turn enterprise reality into context that stays usable over time.
Learn more03Production Readiness
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