AI is moving from answering questions to doing work: planning steps, calling tools and APIs, remembering context, and recovering from mistakes. Evaluating that kind of system is different from checking whether a single response looks good. Agentic evaluation focuses on how an AI agent performs across an entire journey—what it tries, how it decides, and whether it safely achieves the goal at an acceptable cost and speed. Done well, it becomes the operating rhythm that lets you scale AI with confidence rather than hope.
Focus on the journey, not a single reply.
Tie evaluation to outcomes leaders care about: success, risk, speed, and cost.
Make improvement continuous—evaluate, learn, and ship better every week.
Traditional LLM evaluation looks at a single prompt and a single response. That’s useful for content quality, tone, or factuality—but it misses what happens when the model must plan, use tools, and iterate. Agentic evaluation adds that real-world complexity. Think of it as the difference between grading an essay and judging a full project with research, execution, budget, and timeline.
LLM evaluation asks: “Is this answer good?”
Agent evaluation asks: “Did the system understand the goal, choose the right steps, use tools correctly, handle surprises, respect policy, and finish the job on time and budget?”
In practice, you’ll keep both. You still want strong single-turn outputs, but you also need proof that the agent behaves sensibly over multi-step journeys. Without that, pilots feel promising but stall at scale.
Your customers and employees experience AI as part of a workflow—resetting a password, preparing a quote, triaging an alert, reconciling an invoice. Each of those is multi-step and policy constrained. If you only measure message quality, you can ship something that sounds right but performs poorly, costs too much, or violates a policy.
Reliability: Fewer escalations and retries when agents plan and recover well.
Risk control: Safety and privacy metrics give compliance and security teams a seat at the table.
Efficiency: A clear line of sight to latency and cost per successful task keeps budgets intact.
Agentic evaluation measures both process and outcome across the entire session. It checks that the agent understood the intent, laid out a workable plan, picked the right tools, handled missing or conflicting information, respected guardrails, and delivered a result within the agreed time and cost envelope.
Evaluate trajectory, not just a turn.
Use production traces as your source of truth; supplement with targeted synthetic cases.
Treat every score as a teaching signal—feed improvements back into prompts, policies, and (when warranted) model tuning.
Leaders need a small set of KPIs that map directly to business value, with deeper technical metrics available for diagnosis. Start with four, then expand.
Task Adherence (TAS): Did we achieve the goal within the defined constraints?
Safety Policy Index (SPI): Did we stay compliant with privacy, consent, and brand rules?
Latency Success Rate (LSR): Did we meet response-time SLOs for users?
Cost per Successful Task (CST): What did a finished job actually cost?
As you mature, add: Intent Resolution (IRS) for correct understanding, Tool Utilization Efficacy (TUE) and Tool Call Accuracy (TCA) for tool correctness, Memory Coherence (MCR) for useful recall, Plan Quality (PQI) for robust planning, and Conversation Tone Consistency (CTC) for consistent CX.
Begin with real data. Your best test cases live in your own transcripts and logs. Add a small set of synthetic and counterfactual scenarios to stress unusual conditions and keep the loop lightweight so teams use it.
Data & scenarios: Combine production cases with adversarial and counterfactual variations (same intent, different budget/time/policy).
Logging & observability: Capture plans, tool calls, inputs/outputs, timings, errors, and costs using a standard trace format. Redact PII by default.
Automated evaluators: Run checks at three levels—session (TAS/SPI/LSR/CST), trace (TUE/TCA/PQI/MCR), and interaction (valid tool call, state transition).
Human-in-the-loop: Use experts for high-risk sessions and ambiguous cases; their judgments train preferences and refine policy.
This blend keeps you grounded, fast enough for CI/CD, and rigorous enough for risk and audit needs.
You don’t need a specific vendor to adopt these capabilities. Treat them as product requirements for your eval stack.
Model trace analysis: Filter sessions, drill into failure points, and follow the chain from supervisor → agent(s) → tools.
Mix-and-match evaluators: Combine session/trace/interaction checks as needed per product line or intent.
Interactive scorecards: Compare versions, environments, dates, and user segments to spot trends.
Governance & reproducibility: Version datasets, prompts, and models; store audit logs; enforce access controls and retention.
The outcome: leaders see clear dashboards, engineers see root causes, and compliance sees controls.
Think of this loop as an “immune system” for your AI. It senses problems early, responds quickly, and gets stronger over time.
Generate realistic and adversarial scenarios that mirror top user journeys.
Instrument comprehensive traces (plans, tools, memory ops, timings, costs, errors).
Evaluate automatically at session/trace/interaction levels on a daily cadence.
Test components (retrievers, planners, adapters) and end-to-end workflows.
Review selected sessions with domain experts for safety, tone, and bias.
Experiment with A/B or shadow traffic; compare to a locked baseline.
Improve prompts, policies, and (when justified) fine-tune models; re-test the same scenarios.
Guard in real time with policy checks, anomaly detection, and graceful fallbacks; feed incidents back to steps 1–3.
You’ll move faster if you standardize a few simple artifacts. Keep them short and widely shared.
Minimal trace record: session_id; turn; user intent; agent plan; chosen tools; tool_calls (name, args, latency, success); memory reads/writes; response summary; costs; timings; errors.
Scenario catalog: a single place for real cases, edge cases, and constraint flips (budget/time/policy), plus outage and latency simulations.
CI gate policy: block releases that regress on task success or safety, miss latency targets, or raise cost per successful task beyond agreed limits.
You can stand up a credible evaluation backbone in a quarter without boiling the ocean. Focus on a thin slice, then deepen.
Lay the groundwork and set expectations. Pick the top 5–10 intents that matter to customers or internal users and define success for each. Turn on structured tracing in the agent workflows you already have and assemble a starter scenario set from real transcripts plus a handful of stress cases.
Set targets for TAS, SPI, LSR, CST per priority intent.
Enable standardized tracing (plans, tools, timings, costs, errors).
Build a small scenario set (real + adversarial + counterfactual).
Run a nightly evaluator job and publish a simple scorecard.
Shift from ad-hoc checks to systematic gates and shared visibility. This is where release discipline clicks into place.
Add CI/CD gates that fail on safety or task-success regressions.
Launch dashboards by version, environment, date, and intent.
Start HITL reviews on high-risk sessions; collect preference data.
Use your own evaluation deltas to guide practical improvements, not speculative tuning. Add real-time guardrails where the risk-reward is clear.
Update prompts and policies based on measured gaps; fine-tune only where the ROI is obvious.
Enable policy checks before risky tools and anomaly alerts on failure loops, latency spikes, or cost drift.
Run A/B or shadow experiments; track TAS/TUE/MCR/SPI/LSR/CST against baseline.
Create durability. Expand coverage and standardize how changes are controlled and audited.
Grow the scenario library and add distribution-shift and outage suites.
Version datasets, prompts, and models; enforce access, retention, and audit.
Hold a quarterly Eval Review with product, engineering, risk, and CX to set targets and roadmap fixes.
Agentic evaluation should meet enterprise standards from day one. Treat it like any critical business control: clear ownership, transparent evidence, and repeatable processes.
Data protection: PII redaction, role-based access, retention windows, and immutable audit logs.
Policy enforcement: Central guardrails, consent checks, rate limits, and safe fallbacks.
Change control: Versioned scenarios, prompts, and models; reproducible runs linked to release notes.
Most disappointments come from measuring too narrowly or ignoring production reality. Avoid these early and you’ll save months.
Final-answer myopia: Score planning, tool use, and recovery—not just responses.
No real-world coverage: Include messy, multi-step cases pulled from actual usage.
One-metric thinking: Balance success, safety, latency, and cost as a portfolio, not a single number.
Set-and-forget suites: Refresh scenarios and rotate evaluators; keep human spot-checks.
Leaders don’t need graphs for their own sake—they need confidence that AI is doing useful, safe work at a sensible price. When agentic evaluation is embedded, you’ll see the curves move in the right direction and stay there.
Reliability up: TAS trending higher; escalations and retries trending down.
Safety assured: SPI at target; zero P1 policy violations post-release.
Speed and efficiency: LSR hits SLOs; CST flat or falling as volume grows.
Predictable delivery: Releases ship on schedule with eval-gated confidence.
Agentic evaluation turns AI from a promising pilot into a dependable system of work. Start modestly—trace your real journeys, choose four KPIs, and run nightly evaluations—and let the loop teach your agents to plan better, act safer, and deliver business outcomes you can defend to customers, regulators, and the board. Build it in, keep it humane and practical, and improve on a steady cadence.