Agentic systems—autonomous, goal-directed stacks that plan, call tools, observe results, and iterate—are rapidly becoming a core component of modern products. Examples include travel-booking assistants, automated ticket-triage pipelines, and agents that explore product UIs to fix flaky tests. These systems are fundamentally different from single-turn LLM APIs. They have trajectories, internal state, tool calls, and failure modes that unfold over time, often producing emergent behavior that was never explicitly programmed. As a result, testing them requires new mental models, new metrics, and new toolchains.
In this post, we explore the core challenges of testing and validating agentic systems. We believe this will soon become a critical capability for every enterprise deploying agents, and that engineers with experience in this area will be in particularly high demand.
In traditional software engineering, “it works on my machine” became a cliché for environment mismatches. In the era of agentic AI, we have traded that problem for something more subtle: “It worked in the playground.”
Over the last year, the industry has undergone a major architectural shift. We have moved from static LLMs—passive engines waiting for a prompt—to agentic systems that operate as stateful, autonomous loops. These systems can use tools, correct their own errors, and execute multi-step workflows.
This autonomy comes at a high cost. Agents introduce non-determinism directly into the heart of application logic. A test that passes at 9:00 might fail at 9:05 simply because the agent chose a different reasoning path. This is known as the verification gap: the tension between the probabilistic nature of generative AI and the deterministic reliability required in enterprise software.
The challenge is compounded by the fact that agent systems are inherently multi-step and often depend on external tools such as APIs, browsers, and databases that evolve independently. An agent’s correctness is rarely just about its final output. It is also about the path it took: what it decided to do, why it chose a particular tool, whether it detected and corrected errors, and which facts it relied on.
As a result, evaluating agents requires:
- Observability into the agent’s internal trajectory (prompts, tool calls, intermediate outputs)
- Metrics that capture both task success and process quality (efficiency, redundancy, hallucination frequency)
- Infrastructure to simulate or sandbox downstream tools, so tests remain repeatable and affordable
Continuous monitoring to detect regressions caused by model updates, prompt changes, or tool availability
A robust testing program should combine several complementary test types, each targeting a different class of failure.
- Unit tests for components
These are deterministic checks of small, isolated functions such as prompt builders, parsers, tool wrappers, and serialization logic. In LLM systems, this includes validating JSON outputs, verifying that plan extractors return the expected structure for known prompts, and ensuring that tool wrappers correctly retry on errors like “Too Many Requests.” - Integration tests (deterministic scenarios with fakes)
Integration tests run an agent through multi-step scenarios against in-memory fakes or recorded tool responses. Using recorded “tool tapes,” similar to HTTP fixtures, keeps tests repeatable. These tests validate orchestration logic: does the agent call the right tool when given a specific scenario, and does it recover correctly from tool failures? - End-to-end (E2E) scenario tests
E2E tests execute agents against live tools or realistic staging environments, such as sandbox accounts, staging APIs, or headless browsers. These tests validate full system behavior but are brittle and expensive. Keep this set small and focused on representative scenarios. - Trajectory and step-level evaluations
Instead of comparing only final answers, these evaluations examine the agent’s plan, tool-call sequence, and intermediate outputs. This is critical because many failures are process failures: bad plans, redundant tool use, or infinite loops. Modern frameworks increasingly support trajectory capture and scoring. - Regression suites and canaries
Every model or prompt update risks introducing regressions. Maintain a regression suite and run it in CI. Use canary deployments and staged rollouts to observe real-world behavior before full production release.
Live monitoring and synthetic probes
After deployment, synthetic probes periodically execute tasks against production agents to track drift in latency, success rates, hallucination frequency, and tool error recovery. Store detailed logs and maintain a rolling window of historical metrics.
While many of these tests can use traditional tools such as unit test frameworks, mock servers, contract testing, and browser automation, others fundamentally cannot. Open-ended reasoning quality, semantic correctness, planning validity, and safety often resist deterministic assertions.
In these cases, teams increasingly rely on other LLMs as evaluators. These evaluators score responses, analyze trajectories, detect hallucinations, and flag policy violations. This creates a quiet but unavoidable paradox in modern AI engineering: to robustly test agentic systems, we often need to use other agents as judges.
The goal is not to avoid this pattern, but to control it through evaluator diversity, confidence thresholds, and periodic human calibration.
Adversarial testing is essential for the security of agentic systems and deserves special attention. Because agents act autonomously and often have access to privileged tools—databases, email, file systems, or command execution—a single exploit can lead to data leaks, financial loss, or infrastructure damage. Unlike traditional applications, agents can be attacked indirectly through prompt injection, malicious tool outputs, or corrupted memory.
Effective adversarial testing begins with structured threat modeling to map permissions and identify high-risk attack surfaces. Teams should then design targeted adversarial scenarios in sandboxed environments. These include poisoned tool responses, web pages with hidden instruction overrides, and manipulated memory entries.
Fuzzing is especially powerful here. Mutation-based techniques that generate malformed prompts, contradictory instructions, or extreme edge cases can expose brittle reasoning, unsafe tool use, and infinite execution loops.
Automation alone is not enough. Human red-teaming remains indispensable because creative attackers routinely discover exploits that scripted tests miss. All traces from red-team exercises should be captured and converted into regression tests. Beyond prevention, teams must also measure how well agents recover from attacks. A secure agent is not only one that resists manipulation, but one that can detect anomalies, refuse unsafe actions, halt execution, or escalate to a human when confidence in safety breaks down.
When designing scorecards, measure both outcomes and process.
Outcome metrics
- Success rate: binary or graded task completion against ground truth or acceptance criteria
- Quality score: human or model evaluation of the final artifact (email, code patch, booking)
- Cost and latency: API cost per task and wall-clock execution time
- Resource usage: number of tool calls, tokens consumed, and model invocations
Process metrics
- Plan quality: number of planning steps, presence of irrelevant subgoals, and constraint adherence
- Tool usage correctness: proportion of tool calls that were necessary and correct
- Looping indicators: repeated plans or failure to make progress
- Hallucination rate: measured via fact checks or evaluation datasets
Risk and safety metrics
- Policy violations: percentage of responses that violate safety rules
- Attack surface exposure: frequency of actions touching sensitive resources
- Recovery rate: ability to detect and recover from errors or contradictory tool outputs
Design your evaluation program so you can trade off cost and fidelity. Cheap synthetic tests should run on every pull request, while expensive human evaluations and E2E runs can execute nightly or on release candidates.
Several tools and frameworks commonly appear in agent evaluation stacks:
OpenAI Evals
A general framework for building and running evaluations, including model-written evals and A/B comparisons. It is widely used for automated scoring, multi-model comparisons, and evaluator orchestration.
AgentBench
A research-oriented benchmark suite explicitly designed for LLMs as agents. It includes environments for planning, tool use, and web interaction, and is useful for comparing agent capabilities such as long-horizon reasoning.
lm-evaluation-harness (EleutherAI)
A mature framework for running standard NLP benchmarks. While not agent-specific, it is valuable for testing underlying competencies such as summarization, reasoning, and code generation.
LangChain, LangSmith, and Agentevals
LangChain is a common orchestration layer for agents. LangSmith provides observability and evaluation tooling for capturing traces and running trajectory-based evaluators. The agentevals package offers ready-made evaluators for scoring agent behavior.
Agentic AI Observability: LangSmith vs Langfuse vs AgentOps This video provides a visual breakdown of the three major observability platforms discussed, helping you decide which fits your specific stack best.
Browser automation: Playwright and Selenium
When agents interact with web UIs, browser automation is essential. Capture visual diffs and network traces alongside agent trajectories for reproducible tests. AI-assisted test generation can be helpful, but generated tests should always be carefully reviewed.
Agent-to-agent testing platforms
Commercial and open-source platforms now support large-scale multi-agent simulations and safety stress testing, with dashboards for hallucination, toxicity, and bias detection.
A practical blueprint looks like this:
Capture telemetry and traces everywhere
Log prompts, model responses, plan parses, tool calls, and evaluator outputs with unique request IDs. Use structured JSON with timestamps and context identifiers.
Create deterministic fixtures for unit and integration tests
Record typical tool responses as replayable fixtures. Keep them small and stable, snapshotting only contract-level interfaces rather than large third-party payloads.
Define trajectory-level evaluators
Evaluators should take full traces as input and return scores and error classes. Use automated checks wherever possible, with human evaluation reserved for subjective quality.
Run staged test pipelines
Synthetic tests on every pull request, broader integration tests on nightly builds, and a small set of canary tests in production.
Automate human-in-the-loop annotation for edge cases
For tasks without clear gold labels, sample failures, random cases, and near-misses for human review. Store rationale and measure inter-annotator agreement.
Surface key metrics and alerts
Build dashboards for success rate, hallucination rate, tool errors, and cost. Set alerts for significant regressions, such as a five percent drop in success over 24 hours.
The evaluation ecosystem for agentic systems is evolving rapidly, driven by the growing recognition that trajectory-level behavior (not just final outputs) defines real-world reliability. Early evaluation frameworks focused mostly on final-answer correctness. Today, leading platforms increasingly treat the entire agent lifecycle as the primary object of analysis: planning quality, tool selection, error recovery, memory usage, and long-horizon consistency.
We are also seeing a clear shift toward model-written evaluations becoming first-class citizens in production pipelines. Rather than relying exclusively on static rules or human-only review, teams now deploy ensembles of evaluator models that specialize in different dimensions: factuality, safety, instruction adherence, and planning efficiency. These evaluators are becoming more modular, more composable, and easier to audit.
Another major trend is the convergence of orchestration, evaluation, and observability into unified platforms. Instead of stitching together separate systems for tracing, evaluation, and monitoring, teams increasingly expect a single stack that can capture trajectories, run offline and online evaluations, and surface live production metrics with minimal friction. This tight integration enables faster debugging loops and shortens the path from failure detection to root-cause analysis.
Standardization is also beginning to emerge. While still early, we are starting to see shared benchmarks for agent capabilities such as tool reliability, long-horizon planning, and adversarial robustness. Over time, these benchmarks are likely to play a role similar to what GLUE, MMLU, or HELM played for foundation models: providing a common language for progress and trade-offs.
Finally, regulation and enterprise governance will increasingly shape where the field goes next. As agents gain access to sensitive systems, evaluation will no longer be just a quality concern- it will become a compliance requirement. Auditable evaluation pipelines, reproducible test artifacts, and provable safety properties will move from best practices to baseline expectations.
Testing agentic systems is undeniably harder than testing single-turn models. The source of that complexity is not just non-determinism, but the fact that agents are no longer simple functions from input to output. They are dynamic systems that make decisions, revise plans, invoke external tools, and operate across time. Traditional testing approaches struggle precisely because they were never designed to assess behavior, only results.
The key mindset shift is to treat trajectories as the primary unit of correctness. When you capture how an agent thinks, acts, fails, and recovers, testing becomes far more structured. You can debug reasoning errors, quantify inefficiencies, detect unsafe behaviors early, and continuously harden your system against both accidental and adversarial failures.
A mature evaluation program blends multiple layers: fast deterministic unit tests, replayable integration scenarios, selective end-to-end validation, trajectory-level scoring, adversarial stress tests, and targeted human review. Not every task requires the most expensive evaluation, but every production system needs a thoughtful balance between cost, coverage, and confidence.
Perhaps the most important takeaway is this: evaluation is not a one-time investment. As models evolve, tools change, and agents acquire new capabilities, your evaluation strategy must evolve with them. The teams that succeed over the next few years will be the ones that treat evaluation as core infrastructure rather than as an afterthought.
If you are building agentic systems today, invest early in trajectory capture, automated evaluators, and human-in-the-loop feedback. These are not just testing tools; they are the foundation for trust, safety, and long-term scalability in AI-driven systems.
