Compare 13 AI agent evaluation services partners delivering test harnesses and assurance programmes for agentic systems that plan, reason, call tools, and act across multi-step tasks. Engagements cover the task-success benchmark design across closed and open-ended workflows, the tool-use evaluation for function-calling accuracy, parameter correctness, and side-effect safety, the trajectory grading for plan quality and recovery from failure, the cost and latency profiling across model and tool combinations, the prompt-injection and indirect-injection resilience suite, the human-in-the-loop adjudication and inter-rater reliability, the production observability integration with traces, replay and regression baselines, and the alignment to NIST AI Risk Management Framework, EU AI Act conformity-assessment evidence, and ISO 42001 control objectives. Listings cover global SI agentic-AI practices, AI-evaluation pure-plays, foundation-model vendor evaluation teams, and the academic-spinout safety boutiques. No partner pays for placement on this directory.
Agent-evaluation engagements break into four typical workstreams. Benchmark and task-suite design, where the partner inventories the production tasks the agent is expected to complete, derives representative scenarios across the easy, edge, and adversarial distributions, builds the closed-form scoring rubrics for tasks that admit objective grading, and designs the open-ended evaluation pipeline with model-graded or human-graded scoring where rubrics are infeasible. Tool-use and trajectory assessment, where the partner instruments the agent to capture every plan, tool call, observation, and revision, builds the trajectory grader for plan correctness and error recovery, profiles cost and latency across model and tool combinations, and reports the function-call accuracy and side-effect safety metrics. Safety and adversarial testing, where the partner runs the prompt-injection and indirect-injection suites, the data-exfiltration and prompt-leakage tests, the jailbreak and role-confusion attacks, the scheming-and-deception probes for capable models, and the policy-violation suite for the agent's intended deployment context. Production observability, where the partner integrates the evaluation pipeline into the agent runtime, sets up regression baselines, builds the alerting model for task-success drops, and feeds the findings into the iterative training and prompt-design loop.
Three procurement archetypes recur. Global SIs and strategy houses (Accenture, Deloitte, BCG X, EY, plus India-heritage AI sub-units at TCS and Infosys) lead where agent evaluation sits inside a broader enterprise agentic-AI build, the buying centre is the chief AI officer, and the engagement bundles deployment with the assurance work. AI-evaluation pure-plays (Scale AI, Surge AI, Patronus, Humanloop, Arize Phoenix) lead on the deepest technical evaluation, the expert-annotator pools, and the production observability integration where SI evaluation practices are still maturing. Academic-spinout safety boutiques (Haize Labs, Apollo Research) lead on adversarial evaluations, scheming and deception probes, and the long-tail behaviours that mainstream evaluation harnesses miss. Friction point: most enterprises commission agent evaluations as a pre-launch sign-off then stop, but agent behaviour drifts as foundation models upgrade, tool surfaces change, and adversarial techniques improve; treating evaluation as a one-time gate rather than a continuous regression suite is the single largest source of post-launch regression incidents.
For complementary research see AI evaluation platforms, LLM observability, prompt management, AI governance platforms, and synthetic data tools. For adjacent services see LLM evaluation services, AI red-teaming, agentic AI implementation, LLM observability services, AI governance consulting, and ISO 42001 AI management.
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