Research built to change a release decision.
Working protocols for traces, evaluation, release control, production monitoring, and model economics—written for teams accountable for what ships.
Define what must be tested, compared, reviewed, and recorded before a model or prompt change reaches users.
OPEN REPORT →A production evaluation standard for AI systems
Define what must be tested, compared, reviewed, and recorded before a model or prompt change reaches users.
Designing a trace schema for multi-step agents
A practical trace model for prompts, retrieval, tools, model calls, retries, evaluator signals, and user outcomes.
Building regression gates for model and prompt releases
Turn offline evaluation results into release rules that protect important use cases without blocking every change.
Measure cost per successful AI task
Move beyond token totals by connecting provider charges, retries, cache behavior, and evaluation outcomes.
Prompt version governance without slowing engineering
Control prompt changes with readable diffs, evaluation evidence, environment labels, ownership, and rollback history.
Sampling production traffic for online evaluation
Design representative, cost-aware sampling without turning partial telemetry into false confidence.
Calibrating model-based evaluators against expert judgment
Use agreement studies, counterexamples, rubrics, and uncertainty to make LLM-as-a-judge scores decision-worthy.
Designing human review queues for AI quality
Route the right traces to the right reviewers with enough context to make consistent, auditable judgments.
Turn production failures into durable regression datasets
Capture the context, expected behavior, privacy treatment, and ownership needed to reuse real failures safely.
A readiness review for model migrations
Compare quality, behavior, latency, context limits, tool use, safety, and cost before switching providers or model families.
Evaluate retrieval coverage inside the trace
Inspect query construction, candidate sources, ranking, access filters, context selection, and answer grounding as one path.
Measure tool-call reliability across an agent trajectory
Score tool selection, argument validity, execution outcome, recovery behavior, and final task completion.
Set latency budgets for multi-step AI experiences
Allocate time across retrieval, models, tools, evaluators, retries, and streaming while protecting the user-visible outcome.
Understand token, context, and cache economics
Find context growth, repeated prefixes, cache misses, and routing decisions that change cost without improving outcomes.
Using OpenTelemetry for generative AI observability
Adopt open trace context and GenAI semantic conventions while preserving product-specific evaluation and cost fields.
Incident response for production AI quality failures
Define detection, triage, containment, evidence preservation, rollback, and learning for model-driven incidents.
Monitor evaluator drift as carefully as model drift
Detect when scoring behavior changes because rubrics, judge models, prompts, or production distributions have moved.
Experiment design for prompts and models
Use stable baselines, controlled variables, representative datasets, repeated runs, and segment analysis.
Turn red-team findings into regression coverage
Convert adversarial discoveries into versioned cases, controls, and release tests without publishing exploitable detail.
Privacy-aware tracing for AI applications
Collect the evidence needed for debugging and evaluation without turning telemetry into an uncontrolled data copy.
What belongs in an AI release review
A concise evidence packet for quality, behavior, cost, latency, security, ownership, and rollback.
A governance model for enterprise AI spend
Connect provider invoices, engineering telemetry, product ownership, quality, and business outcomes.
Monitor model routing as a production control
Evaluate quality, latency, cost, fallback behavior, and segment effects when traffic moves between models.
The operating model behind a durable evaluation program
Define ownership, cadence, evidence, escalation, change control, and improvement across engineering and domain teams.
Use one production release to test the operating model.
A technical assessment translates these engineering patterns into trace coverage, evaluation policy, release evidence, and measurable pilot acceptance.