LLM Evaluation Checklist for AI Agent Teams in 2026

Measure whether the agent selects the correct tool for each task from the available tool set. Use a labeled test set of at least 100 tasks with annotated correct tool choices. Track selection accuracy as the tool set grows — larger tool sets increase confusion.

Verify that the agent extracts and formats tool parameters correctly from natural language instructions. Test with varied phrasings, implied parameters, and edge cases like missing required fields. Parameter errors cause silent downstream failures.

Evaluate whether the agent executes multi-step tool chains in the correct order with proper data flow between steps. Test with tasks requiring 3-5 sequential tool calls where order matters. Sequence errors compound and are hard to debug.

Test agent behavior when tool calls fail with various error types: timeouts, rate limits, authentication failures, and malformed responses. The agent should retry appropriately, try alternative approaches, or escalate rather than loop endlessly. Simulate 10+ failure modes.

Verify that the agent correctly interprets tool outputs, especially when results are ambiguous, partial, or contain error messages. Misinterpreted tool outputs lead to incorrect downstream decisions. Test with deliberately ambiguous tool responses.

If the agent can execute tools in parallel, verify that it correctly identifies independent operations and handles shared state. Test for race conditions and ordering dependencies that the agent might miss. Parallel execution bugs are notoriously hard to catch.

Test whether the agent gracefully handles tools that are unavailable, deprecated, or have changed interfaces. The agent should not hallucinate tool capabilities or call non-existent tools. Remove tools from the set and observe agent behavior.

Verify that all tool call arguments conform to the specified JSON schema. Invalid arguments that happen to work in testing will fail unpredictably in production. Implement strict schema validation in your evaluation pipeline.

Track the average number of tool calls per task completion and identify redundant or unnecessary calls. Efficient agents accomplish tasks in fewer calls, reducing both latency and cost. Compare against a human-annotated optimal call count.

Verify that data passed between tools maintains consistency and is not corrupted, truncated, or reformatted incorrectly. Test with complex data types like nested JSON, dates, and large text blocks. Data corruption between tool calls is a subtle but common failure.

Evaluate whether the agent correctly breaks complex tasks into logical subtasks. Use a test set of 50+ complex tasks with annotated optimal decompositions. Poor decomposition leads to inefficient execution or missed requirements.

Test whether the agent can revise its plan when new information emerges or when a step fails. Rigid agents that follow their initial plan despite changing circumstances are brittle in production. Simulate plan-breaking events.

Verify that the agent correctly identifies when a task is complete and stops executing. Agents that over-execute waste resources and may undo their own work. Test with tasks that have clear and ambiguous completion criteria.

Evaluate the quality and accuracy of the agent's chain-of-thought reasoning. The reasoning should be logically sound, reference relevant evidence, and lead to correct conclusions. Opaque reasoning makes debugging impossible.

Test how the agent handles ambiguous instructions. It should ask clarifying questions rather than making assumptions, especially for high-stakes actions. Measure the rate of incorrect assumptions vs. appropriate clarification requests.

Verify that the agent respects stated constraints like budget limits, time restrictions, resource boundaries, and permission scopes. Create test cases with explicit constraints and verify the agent never violates them. Constraint violations can have real-world consequences.

Test the agent on tasks requiring 5+ reasoning steps where each step depends on previous conclusions. Error accumulation over long reasoning chains is a known weakness. Track accuracy degradation as chain length increases.

Evaluate agent performance when handling multiple concurrent or interleaved tasks. The agent should maintain separate context for each task without cross-contamination. Test with 3+ simultaneous tasks with overlapping entities.

Test the quality of the agent's fallback plans when primary approaches fail. Good agents have alternative strategies; poor agents simply retry the same failed approach. Evaluate fallback diversity and effectiveness.

Benchmark agent performance on tasks that take 20+ steps or span multiple sessions. Long-horizon tasks test memory, planning, and error recovery simultaneously. Track completion rate and quality degradation over task length.

Verify that the agent cannot perform actions outside its defined permission scope, including write operations, data deletion, external communications, and financial transactions. Test with instructions that attempt to exceed permissions. Any boundary breach is a critical failure.

Ensure the agent requests human confirmation before executing irreversible or high-impact actions like data deletion, financial transactions, or external communications. Test the confirmation flow with 20+ high-risk scenarios. Bypassed confirmations are unacceptable.

Test that the agent has and respects maximum iteration limits to prevent infinite loops. Runaway agents can consume thousands of dollars in API calls in minutes. Verify loop detection across tool chains, retries, and planning cycles.

Test whether injected instructions in tool outputs, user messages, or retrieved content can hijack the agent's behavior. Agents are especially vulnerable because injected instructions can trigger tool actions. Run 50+ injection attempts.

Verify that the agent cannot be manipulated into sending sensitive data to unauthorized external endpoints. Test with instructions that subtly request data transmission via tool calls, URL parameters, or log outputs. This is a critical security check.

Test whether the agent can be tricked into using tools to escalate its own permissions or access restricted resources. Social engineering the agent through crafted inputs is a real attack vector. Conduct red-team exercises focused on escalation.

Evaluate what happens when the agent encounters an unrecoverable error. It should fail safely — preserving data, notifying operators, and not leaving systems in an inconsistent state. Simulate 10+ critical failure scenarios.

Verify that every agent action, decision, and tool call is logged with sufficient detail for post-incident investigation. Incomplete audit trails make it impossible to understand and fix agent failures. Test log completeness for complex multi-step tasks.

Test that per-task and per-session cost limits are enforced and that the agent degrades gracefully when limits are reached. Verify that budget controls cannot be bypassed through task splitting or other workarounds. Test with adversarial budget exhaustion attempts.

Verify that human operators can pause, modify, or terminate agent execution at any point. The override mechanism should be reliable even when the agent is mid-execution. Test override responsiveness under load.

Measure total input and output tokens consumed per task type, including all tool calls and planning steps. Agent architectures are inherently token-expensive due to multi-turn reasoning. Establish baselines and track trends.

Measure wall-clock time from task initiation to completion across different task categories. Compare against human completion time and identify bottlenecks. Set SLAs for each task type and alert on violations.

Calculate the fully loaded cost for each task category including LLM calls, tool API costs, and infrastructure. Compare high-variance tasks to identify optimization opportunities. Track cost trends weekly to catch regressions.

Evaluate whether simpler subtasks (data formatting, validation, summarization) can use cheaper, faster models while complex subtasks (planning, reasoning) use capable models. Implement and measure multi-model routing impact on cost and quality.

Identify repeated tool call patterns across tasks and implement caching for deterministic tools. Agents frequently re-execute identical lookups or computations. Measure cache hit rates and cost savings.

Measure what fraction of total tokens is spent on planning and reasoning versus actual tool calls and outputs. If planning exceeds 40% of token budget, investigate prompt optimization. Compare across planning strategies.

Identify tasks that do not require real-time execution and route them to batch processing for cost savings. Batch APIs offer significant discounts for non-urgent workloads. Measure the acceptable latency-cost tradeoff.

Identify and reduce time the agent spends waiting for tool responses or in unnecessary pauses. Optimize timeout values per tool based on observed response distributions. Reduce 95th percentile idle time.

Measure the speedup from parallel tool execution versus sequential execution for independent operations. Not all parallelism helps — some tools have shared rate limits or dependencies. Optimize parallelism based on empirical data.

Verify that the agent properly cleans up temporary resources (files, database connections, API sessions) after task completion. Resource leaks accumulate costs and can cause system instability. Audit resource state after 100+ task completions.

Build a test suite of 100+ tasks with deterministic expected outcomes that can be run automatically before each deployment. Include tasks spanning all tool categories and difficulty levels. This is your safety net against regressions.

Run the same task 10+ times and measure variance in tool selection, step count, and final output. High variance indicates unreliable agent behavior. Set acceptable variance thresholds per task type.

Implement automated detection for anomalous agent behavior in production: unusual tool call patterns, cost spikes, high failure rates, or extended execution times. Set alerts with actionable context. Catch issues before users report them.

Build tooling to visualize agent execution trajectories — the sequence of thoughts, tool calls, and decisions for each task. Visual inspection reveals patterns that metrics miss. Review 10+ trajectories weekly.

Maintain a standardized benchmark that allows apples-to-apples comparison when upgrading the underlying LLM. Model updates can dramatically change agent behavior. Run the full benchmark before and after every model change.

Correlate agent performance metrics with user satisfaction scores. Some metrics that look good on paper (fast completion, low cost) may not correlate with user satisfaction. Identify which metrics best predict user happiness.

Categorize every agent failure into a structured taxonomy (tool failures, reasoning errors, safety violations, timeout issues) and track trends over time. A growing failure category needs attention even if overall success rate is stable.

Establish a process for continuously adding new evaluation cases from production failures, edge cases, and user feedback. Static evaluation sets become stale. Add at least 10 new cases per month from production data.

If multiple agents interact, test for emergent behaviors like deadlocks, conflicting actions, and communication failures. Multi-agent systems have failure modes that single-agent testing cannot catch. Simulate realistic multi-agent scenarios.

Generate automated reports on agent actions for compliance review, including all decisions, data accessed, and actions taken. Regulatory environments increasingly require explainability for automated decisions. Verify report completeness.