Designed a reliability lab for agentic LLM workflows with scenario-based evaluations, runtime guardrails, and tool-call observability. The platform stress-tests multi-step tasks, quantifies failure modes, and provides deployment gates before shipping new prompts, tools, or model versions.
The system continuously evaluates plan quality, tool correctness, grounding, and recovery behavior. Model variants are promoted only if they pass hard reliability gates.
Balanced eval pack with easy tasks, adversarial prompts, and long-horizon tool workflows.
Agent plans, calls tools, and adapts after each observation to complete multi-step objectives.
Logs tool arguments, outputs, retries, and step transitions for deep debugging and scoring.
Combines rubric-based checks with model-as-judge scoring for correctness, grounding, and safety.
Computes release risk from failure severity, frequency, and blast radius across scenario families.
Applies policy checks and intervention logic: retry with constraints, block unsafe actions, or escalate.
Only variants that pass mandatory reliability thresholds are promoted to production traffic.
New plot set focuses on tradeoffs: quality vs cost, reliability vs speed, and failure concentration.
Mixed chart: bars for success, line for cost
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Bubble size represents average token footprint
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Distribution of top reliability regressions
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Stability index and intervention windows
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How agent effort is distributed across tools
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Scatter plot of severity score vs containment success
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Agentic systems fail unpredictably without systematic evaluation, guardrails, and release governance.
Reliability lab with scenario evals, tool-trace diagnostics, failure taxonomy, and deploy/hold gating.
Higher task completion rates, fewer critical failures, and safer production upgrades for agent workflows.
I can help teams operationalize evals and guardrails so agent systems are measurable, safe, and shippable.
Scenario pack, baseline reliability metrics, and dashboard for decision-ready iteration.
Regression gates, policy checks, and post-deploy monitoring for safe agent updates.
Evaluation architecture and implementation support for internal AI platform teams.
Traffic simulation and intervention planning with multi-modal forecasting.
Grounded QA and citation-backed reasoning over enterprise docs.
Condition-aware fusion for robust autonomous perception.
Unified BEV architecture for 3D detection at edge latency.