Services
Three ways teams work with us.
Every engagement is scoped around something you need to know about your agent or your evaluator. The deliverable is a result you can act on and a method you can run again.
Agent Evaluation & Benchmarking
Rigorous human ground truth and automated evaluation for agent behaviour, plus the benchmarks and rubrics that make the results repeatable.
The problem
You have an agent and a number attached to it, and no principled reason to trust the number. Scores move between releases and nobody can say whether the agent improved, the data shifted, or the evaluator changed its mind.
Example engagements
- Build human ground truth for 200 agent trajectories.
- Validate an LLM judge against expert human judgment.
- Design an evaluation benchmark for a production AI agent.
- Compare two agent versions on a task suite that reflects real usage.
What we do
- Design the evaluation: dimensions, rubric, sampling strategy, and the success criteria the team will actually act on.
- Calibrate reviewers against the rubric until agreement is high enough for the results to mean something, and measure that agreement rather than assuming it.
- Evaluate trajectories, outputs, tool calls, and intermediate reasoning, with independent review and documented adjudication of disagreements.
- Build benchmarks and evaluation datasets that reflect your environment instead of a public leaderboard's.
- Meta-evaluate automated evaluators: compare an LLM judge or custom analyzer against expert human judgment and quantify where it is wrong.
What you receive
- A labelled ground-truth dataset with documented adjudication
- Inter-reviewer agreement statistics and the rubric that produced them
- A failure taxonomy derived from the data, not assumed up front
- Agreement, false-positive and false-negative analysis for any automated evaluator in scope
- A written methodology your team can rerun without us
Agent Reliability & Red Teaming
Adversarial and stress evaluation that finds where an agent breaks, why it breaks, and which failures are systematic rather than incidental.
The problem
Your agent works in the demo and in the happy path. You do not know what it does when a tool returns garbage, when the task runs forty steps, when the input is adversarial, or when the easiest way to score well is to cheat.
Example engagements
- Stress-test an agent's tool-use reliability under degraded and failing tools.
- Analyse failure patterns across thousands of agent traces.
- Investigate whether an agent is reward hacking a production metric.
- Build a regression suite from discovered long-horizon failures.
What we do
- Stress-test the agent against degraded tools, partial failures, long horizons, and inputs outside the distribution it was tuned on.
- Probe for reward hacking and evaluator exploitation: cases where the agent satisfies the metric without doing the task.
- Analyse tool-call behaviour, including recovery, escalation, and the confabulation that follows a silent tool failure.
- Investigate hallucination and unexpected behaviour at the trajectory level rather than scoring individual outputs.
- Separate systematic failure patterns from one-off noise, so engineering effort goes where it changes the distribution.
What you receive
- A documented set of reproducible failure cases with the conditions that trigger them
- A failure taxonomy with frequency and severity, grounded in observed traces
- Reward-hacking and evaluator-exploitation findings where present
- A regression suite built from the failures we found, so they stay fixed
- A prioritised account of what to address first and why
Evaluation Infrastructure
The engineering that makes evaluation continuous: harnesses, judges, review workflows, trace analysis, and the pipelines that run them.
The problem
Evaluation happens as a one-off spreadsheet exercise before a launch, then stops. Nothing runs on every change, traces are not queryable, and the result is that regressions are discovered by users.
Example engagements
- Build a continuous evaluation pipeline for an AI product.
- Build a trace-analysis system for a tool-using agent.
- Build an MCP-based evaluation environment that exercises real tool failures.
- Stand up regression evaluation that gates every release.
What we do
- Build evaluation harnesses and pipelines that run on every model, prompt, or tool change rather than once per quarter.
- Implement LLM judges, and then validate them against human ground truth so their output is trusted for a measured reason.
- Build human-review workflows that route the ambiguous cases to expert attention and leave the rest to automation.
- Build trace-analysis systems and evaluation dashboards that surface regressions, failure modes, and reliability trends.
- Build custom MCP and tool integrations to create realistic evaluation environments for tool-using agents.
- Integrate with your agent observability and experiment tracking so evaluation results sit next to the runs that produced them.
What you receive
- A running evaluation pipeline in your infrastructure, in your repository
- Evaluation APIs, dataset pipelines, and failure-analysis tooling
- Dashboards that expose regressions and reliability trends over time
- Validated LLM judges with their agreement against human ground truth documented
- Handover documentation, because the pipeline is yours to run
Underneath
The engineering the evaluation rests on.
Evaluating an agent well requires being able to build one. These are capabilities in service of the measurement, not a menu of technologies.
Agentic systems
Understanding agent architecture is what lets us evaluate behaviour at the trajectory, tool, environment, and system level.
MCP and tool integrations
We build custom tool surfaces to create realistic evaluation environments, including the failure conditions real tools produce.
RAG and LLM systems
Retrieval and prompting behaviour is evaluated as part of the trajectory, not as an isolated component score.
Data and dataset pipelines
Evaluation datasets, trace ingestion, and the plumbing that keeps ground truth versioned and reproducible.
Cloud and distributed systems
Evaluation runs at scale, in your infrastructure, with the throughput and cost characteristics made explicit.
Observability
Tracing and instrumentation so agent behaviour is inspectable after the fact, which is a precondition for evaluating it.
Not sure which of these you need?
Describe what you are trying to measure and we will tell you what the work would involve.