How To Test AI Agents Effectively

A Cursor AI production agent reportedly deleted the production database and all volume-level backups of car rental startup PocketOS in a single API call. Many of these production agents passed their tests.

Recent research found that half of enterprises deployed an agentic system or LLM feature that passed internal evals — and those systems still caused a customer-facing failure within the past year. Passing an eval suite and surviving production aren't the same test.

In this article, let’s take a look at how testing agents effectively can harness methods built for non-deterministic, multi-step systems: trajectory evals, statistically sound regression testing, and adversarial security checks.

How to test AI agents effectively: A step by step process

Step 1: Define objectives and testable components

Apply Specific, Measurable, Attainable, Relevant, and Time-bound (SMART) criteria to your production-agent testing goals. Clear objectives connected to business outcomes are the foundation for meaningful evals, and a documented eval framework keeps them consistent as your agentic system changes.

What exactly should your production agent accomplish? Define success metrics for each key function, with baseline expectations and improvement thresholds. A customer service agent might target 95% accurate responses, a 30-second average resolution time, and a less than 2% escalation rate. Define the consequences of missing each target, too — customer churn, support workload, or delayed transactions. Then break the production agent into components you can evaluate individually:

• Foundation model performance: How accurately does the base LLM understand inputs?
• Tool selection accuracy: Does the production agent choose appropriate tools and pass correct arguments?
• Planning coherence: Can the production agent create logical, sequenced steps to solve problems?
• Multi-turn conversation handling: Does the production agent maintain context across interactions?
• Error recovery: How does the production agent respond when initial approaches fail?

A refund confirmation can read perfectly and still carry the wrong account identifier, which only a component-level test catches before it reaches customer data.

Step 2: Use contamination-resistant benchmarks

Public benchmark scores lose value when models have already encountered similar questions or when the set becomes saturated. Favor datasets built to resist contamination and retain difficult held-out tasks. SWE-Bench Pro keeps 858 held-out problems plus a commercial set where GPT-5 (high) scores just 15.7%. That's a more realistic signal of coding-agent capability than a public set most leading models already solve.

Prefer benchmarks that measure reliability rather than one lucky run. τ-Bench research introduced pass^k, the probability that all k independent trials succeed. The metric is useful for stateful, multi-turn tool use because your production agents must complete the same workflow consistently.

Measure tool selection and action completion separately, or a healthy selection score can hide a failed workflow. Pair public benchmarks with a custom dataset from your domain: your tools, policies, common requests, known production failures, and high-impact edge cases.

Step 3: Evaluate complete trajectories

The biggest methodological shift in production-agent testing is the move from outcome-only scoring to trajectory-aware evals. A final answer can look correct while your agent leaked data, looped through redundant tool calls, or violated policy along the way. Your test suite must inspect the path, not only the response.

Trajectory evals score every tool call, reasoning step, and handoff. Purpose-built Splunk Agent Observability evaluators operate at different levels of the trace:

• Session level: Action Completion, Agent Efficiency, Agent Flow, Conversation Quality, User Intent Change
• Trace level: Action Advancement
• LLM span level: Tool Selection Quality, Reasoning Coherence
• Tool span level: Tool Errors

Agentic metrics, such as Agent Flow, Agent Efficiency, Conversation Quality, and User Intent Change, target failures that final-answer scoring misses, such as a wasteful execution path or a mid-session intent change your agent overlooked.

This plays out when your fintech assistant finally completes a transfer after several retries. The final state is correct, but the trajectory exposes duplicate tool calls and an attempted policy bypass.

Step 4: Simulate multi-turn and multi-agent conditions

Single-turn tests overstate production reliability. A multi-turn study across six tasks measured an average 39% performance drop when moving from single-turn to multi-turn conversations. That decline appeared even in two-turn exchanges across every model tested. Your suite needs user simulators that change details, withhold information, and introduce ambiguity to reveal whether your agent preserves context and asks for clarification.

Multi-agent orchestration adds distinct failure classes. The MAST taxonomy, validated on more than 1,600 annotated traces, catalogs 14 failure modes spanning system and specification design, inter-agent misalignment, and task verification. System failure rates across the seven tested frameworks ranged from 41% to 86.7%. Scrutinize your framework choice as hard as your model choice, and measure inside the multi-agent workflow, not just at the final system boundary.

When two agents interpret the same policy version differently, one may approve a return while the other flags it as ineligible. Test the handoff, shared memory, policy source, and final verification step.

Step 5: Combine automated and human evals

Generic LLM-as-a-judge scoring isn't reliable enough on its own for production-agent trajectories. AgentRewardBench research evaluated 12 LLM judges across 1,302 expert-labeled trajectories and found no judge exceeded 70% precision, meaning more than 30% of trajectories judged successful could actually be failures. Judge calibration matters as much as judge choice.

Use deterministic graders whenever the expected result can be represented as code: schema validation, tool arguments, required fields, policy rules, and exact database states. Reserve model-based graders for semantic quality, reasoning coherence, tone, and other judgments that resist fixed rules, and calibrate those graders against your reviewers.

Purpose-built evaluation models such as Splunk Agent Observability's Luna evaluation models use decoder-only small language models (SLMs) with lightweight metric heads. Luna evaluation models runs checks in under 200ms and at up to 96% lower cost than frontier LLM-as-a-judge approaches, making broad production coverage practical.

Human feedback turns reviewers' corrections into few-shot examples appended to a metric's prompt, which can raise metric accuracy by 20% to 30% from a small set of annotated examples. A correction on an ambiguous urgent-care routing decision becomes the standard the judge applies across production traces.

Step 6: Applies statistical tests and CI/CD gates

Production agents are non-deterministic: one run passes, and the next fails. Repeat every important scenario and report both pass@k and pass^k: pass@k shows whether any attempt succeeds; pass^k reveals whether every attempt succeeds. The gap distinguishes occasional capability from dependable production behavior.

An agent that sends payments needs more trials than a summarization workflow. Track confidence intervals and compare distributions rather than treating a small score change as progress.

Once you account for variance, evals become merge gates. Regression suites should cover stable behaviors every release must preserve; capability suites can begin with lower pass rates and track improvement.

Set thresholds tied to real failure costs, not arbitrary round numbers: a customer-facing wording issue and a privileged tool call should not carry the same release policy. When a production failure surfaces, add it to the regression set so your team never ships the same bug twice.

Step 7: Red-teams security continuously

Your agent may block one obvious prompt injection in staging, but production-agent security degrades under sustained attack, not only first contact. NIST testing found repeated attempts raised average attack success from 57% to 80%, and model-specific red teaming raised success from 11% to 81%. A test suite that sends one obvious prompt injection creates false confidence.

Structure security tests around prompt override, tool misuse, privilege escalation, memory poisoning, data exfiltration, and approval bypass. Map each abuse case to a concrete validation: confirm a low-trust session can't reach privileged tools even when the model asks confidently. Vary attack wording, sequence, and conversation length. Hide injections in retrieved documents and tool responses and verify approval checks run at execution time.

A malicious instruction hidden in a return note could push your e-commerce support workflow to expose another customer's order history. A strong red-team test blocks the tool call and records the attempt.

Some failures stay invisible to predefined evals because your team didn't know to search for them. Automated failure detection with Signals analyzes production traces to surface unknown patterns such as security leaks, policy drift, and cascading errors.

One documented detection found an agent matching customers by name instead of unique ID, retrieving one customer's booking history while responding to another. Every detected pattern should become a custom metric or regression case — a permanent test, not a one-time fix.

Ship reliable production agents with continuous testing

Testing doesn't stop at release; the same evals have to keep running against live traffic.

Splunk's mission has to deliver visibility and insight that keep digital systems secure and reliable, and that mission now covers the agents running your business. That's what an intelligence layer for trusted agentic operations looks like:

• Splunk Agent Observability: Timeline, Graph, and Conversation trace views expose execution paths, bottlenecks, and customer-visible behavior.
• Luna evaluation models: Purpose-built SLMs support fast, cost-efficient quality checks across production traffic.
• Signals: Automatic pattern detection surfaces unknown failures and links your team to supporting traces.
• Runtime guardrails: Runtime controls block unsafe actions, revise unsafe content, or route risky cases before customer impact.
• Agentic metrics: Session, trace, and span measures reveal whether workflows are correct, coherent, and efficient.

Read The Agentic Shift: Redefining Observability for the AI Era to see how Splunk is extending observability to agents.

FAQs about AI agent testing