Rigorous analysis for AI agents across enterprise workflows. Understand behavior, find failure modes, and improve with evidence.
Models answer prompts. Agents do work: they fetch data, use tools, follow steps, and hand off to people and systems.
New LLMs, APIs, and policies shift behavior. An agent that worked last month can drift today.
Permissions, rate limits, stale context, and latency spikes break tasks—even if single replies look good.
Understand how your agent behaves end-to-end on real-world tasks.
Set up tasks that mirror your work—for example, a customer service agent handles a complex escalation, queries multiple systems, and routes to the right team. We analyze tool use, retrieval accuracy, multi-step planning, error recovery, guardrails, and handoffs to humans or systems.
Measure incremental performance when industry-leading agents are paired with your context solutions.
Run identical tasks with or without your context solutions to quantify the performance delta.
See how you stack up across LLMs under the same tasks.
Run identical tasks across different versions of your agent, multiple LLMs, or orchestration patterns. Keep data and tools constant so differences are meaningful.
Prove your Model Context Protocol setup is accurate, reliable, and secure.
We evaluate context exchange (is the right resource fetched and fresh?), schema conformance, tool-call arguments, timeouts and error handling, resume/rollback behavior, permission boundaries, and observability.
Parallelize analysis tasks across thousands of environments.
Orchestrate and automate analysis tasks at scale using Platform infrastructure to maximize throughput and depth of analysis.
We bring proven rubrics, sample tasks, and truth-set patterns—so you get credible results quickly.
Apples-to-apples comparisons across LLMs and agent frameworks you can share with leadership and stakeholders.
Optional re-evaluation tied to LLM upgrades or product releases keeps your agent trustworthy over time.