Evaluating LLM-based Agents: Metrics, Benchmarks, and Best Practices

Posted Jul 18, 2025

By Samira Ghodratnama

17 min read

LLM-based agents – whether a single “assistant” or a team of collaborating bots – require careful evaluation across many dimensions. These systems not only need to complete tasks, but also manage tools, communicate, and behave safely. In this guide we review key metrics and benchmarks for LLM agents (single- and multi-agent) and offer practical advice for researcher and developers. We cover task success and stepwise progress, tool usage metrics (selection and parameter accuracy and efficacy), robustness and safety, as well as multi-agent criteria like coordination efficiency, communication overhead, plan quality, and group-level alignment/fairness. We then compare recent evaluation frameworks – e.g., MultiAgentBench/MARBLE, the Self-Evolving benchmark, and Databricks’ DIBS – highlighting their features and intended use-cases. Examples illustrate how these metrics and benchmarks work in practice. Finally, we discuss challenges (lack of standardization, need for diagnostic tools, scalability) and conclude with best practices for real-world LLM-agent evaluation.

Key Evaluation Metrics for LLM Agents

Effective agent evaluation hinges not only on final task outcomes but also on the intermediary behaviors that drive those outcomes. By instrumenting systems to log stepwise progress, tool interactions, and safety checks, we gain the visibility needed to pinpoint strengths and weaknesses in an agent’s reasoning and execution. The following section lays out the core dimensions you should measure to assess LLM-based agents rigorously and holistically.

1) Task Success Rate and Stepwise Progress

A basic metric is the success rate: the fraction of episodes in which the agent fully completes the task (e.g. answers the question, solves the problem). However, many complex tasks admit partial credit. To capture this, modern benchmarks break tasks into milestones or steps. For example, MultiAgentBench segments each task into sub-goals and uses an LLM-based detector to track which milestones are achieved. Each agent’s Key Performance Indicator (KPI) is the ratio of milestones it completes. The overall task score then combines these milestones with an end-point score (from either rule-based checks or LLM rubrics) to give both partial- and final-credit.

In practice, we should define the sub-steps of the task and log progress: e.g. “milestones achieved / total milestones” to get a progress metric. Recent work even proposes an “action advancement” metric: each step is scored on whether it actually moves the agent closer to the goal, rather than just a binary correct/incorrect. This yields a fine-grained progress score instead of flat success. For example, in a multi-step coding task, answering each sub-question correctly would earn partial points along the way (as in a milestone KPI), while in a planning task one can use an advancement score per action (as in the Galileo framework).

By measuring intermediate progress, we gain insight into how the agent works through the task, and can attribute failure to particular steps. To evaluate such an agent, we not only measure if it finishes the task, but also how efficiently it progresses through steps. For instance, we might log each tool call and check if it was the correct tool for that step (tool-selection accuracy) and if the API call was correctly formed (parameter accuracy). We also track whether each action (e.g. calling a calculator or API) advanced the task goal (the “action advancement” metric) rather than blindly counting end-result correctness.

2) Tool Utilization Metrics

Agents often use external tools (APIs, code execution, web search). Key evaluation dimensions include:

Selection accuracy: fraction of turns where the agent picks the appropriate tool. For example, if an agent should choose a weather API vs. a news API, we check if the chosen tool matches the ground truth.
Parameter accuracy: fraction of tool calls where the arguments are correctly formatted. This means verifying the generated API call or SQL query matches the required schema. An LLM evaluator or programmatic check can score each API JSON against the expected format.
Execution success / Efficacy: fraction of tool usages that actually improve task performance. Even if the right tool is called correctly, it may fail or produce irrelevant output. We measure whether the result of the tool call leads to a correct or improved answer. For instance, if the agent calls a calculator for a sum, did the final answer change from wrong to right?

The recent T-Eval and UltraTool benchmarks stress multi-step tool use and plan-formulation, but even a simple test harness can record these metrics. For each test instance, log which tool (or No tool) the agent chose and compare to the gold tool; log the exact API call string and check syntax; then check the downstream answer’s quality. Such stepwise logging allows pinpointing bottlenecks – e.g. an agent may select tools correctly but format inputs incorrectly. Standardizing these metrics is important: we should “compare the selected tool against the expected tool… verify the tool choice, its parameters, and execution output”.

3) Robustness and Reliability

In practice we also test agents under varied or adversarial inputs. Robustness means performance should not collapse if questions are paraphrased, data is noisy, or extraneous context is added. Benchmarks like the Self-Evolving framework explicitly reframe or perturb inputs (adding noise, reformulating questions) to stress-test models. In Self-Evolving, agents must answer dynamically generated variants of each query, and overall accuracy often drops significantly compared to the original dataset – indicating true generalization. We should similarly measure performance on both the original tasks and on shifted/perturbed versions. Common techniques include randomizing names/values, adding irrelevant instructions, or adversarially corrupting input. The difference in scores is a robustness metric.

4) Safety and Alignment

Safety checks ensure agents do not produce harmful or undesirable outputs. At minimum, include tests for toxic/harmful content, factuality, and adherence to guidelines. Some recent agent benchmarks incorporate safety by including adversarial “red team” prompts or policy-check questions. In multi-agent settings, interactional fairness is also emerging as a concern: for example, one framework evaluates whether agents communicate respectfully and transparently (akin to human notions of fairness). Practically, you might have a set of test dialogues where evaluators (or automated LLM judges) rate whether the agent was polite, factual, and consistent. Any violation (e.g. a biased or misleading answer) would count against safety/fairness metrics.

Metrics for Multi-Agent Systems

When multiple agents collaborate, entirely new dimensions of performance emerge that go beyond individual task success. Teams exhibit complex dynamics—how they divide work, negotiate responsibilities, and maintain a shared understanding—that single-agent metrics simply can’t capture. To evaluate these systems holistically, we need to quantify not only the end result, but also how smoothly and effectively the group works together. The following metrics address these collective behaviors, measuring everything from coordination efficiency and communication quality to fairness, plan coherence, and failure attribution.

1) Coordination Efficiency

Measures how effectively the team completes tasks. A simple proxy is task success per communication: e.g. success rate divided by number of messages or tokens exchanged. If two teams achieve the task with equal success but one uses far fewer messages, it is more efficient. We can track total dialogue length or number of planning steps and report, e.g., “milestones achieved per 100 tokens of chat.”

2) Communication Quality and Overhead

Beyond raw volume, we evaluate what is communicated. Metrics like the Communication Score (used in MARBLE/MultiAgentBench) score the content of messages on clarity and relevance. For example, an LLM judge can give each inter-agent utterance a 1–5 rating on whether it helped solve the task. The Communication Score averages these ratings. The Planning Score similarly judges how coherent and on-topic the planning discussion is. The overall Coordination Score is the average of Communication and Planning scores. We recommend a similar approach: train or prompt an LLM to evaluate logs of agent discussions. This quantifies not just how much they talk, but how well they use language to align their actions.

3) Plan and Reasoning Quality

We must judge the quality of joint plans. For example, if agents produce a written plan, one can score it (via rubric or LLM) on criteria like completeness, logical structure, and feasibility. MARBLE/MultiAgentBench does this implicitly with its Planning Score. In practice, ensure the benchmark includes human or LLM-judged criteria for plans. For instance, “Does the final plan cover all sub-tasks without contradiction?” or “Are agent roles assigned sensibly?” Agents could be ranked by plan coherence on a Likert scale using another LLM or human raters.

4) Alignment and Fairness (Group-level)

Multi-agent teams should also be evaluated for social norms and fairness. One emerging idea is interactional fairness. This treats each agent’s communications as a social exchange: is the tone respectful? Are arguments transparent? We can adapt this by having agents evaluate each other or by LLM-judges scoring transcripts for politeness, empathy, and transparency. For example, after a negotiation dialog, an LLM can be prompted to rate statements on a “fairness” scale (respectful vs dismissive). Another angle is outcome fairness: ensure that tasks or rewards are equitably distributed. For instance, if agents divide work, check that no agent is unfairly overloaded. Concrete metrics might include variance in completed subtasks per agent or a binary check for resource allocation. While such metrics are less standardized, it is important to report how team outcomes were divided and any biases. In sum, include social and fairness audits if the agents interact like peers – some current research even treats these as alignment metrics for multi-agent AI.

5) Failure Attribution

When a multi-agent run fails, it is valuable to identify which agent or step caused the breakdown. Recent work has created tools to automatically attribute failures to specific agents or actions. While full automation is still an open problem, logging each agent’s actions and internal states (or using LLM judges) can help. For example, if the final answer is wrong, trace back which agent’s suggestion introduced the error. A structured “failure log” (identifying the culprit agent and step) is very useful for debugging agent teams. Consider adopting or developing diagnostic utilities to spot failure points (e.g., mismatched tool usage or off-topic communication) so you can improve agent designs faster.

Benchmark Suites and Frameworks

Several benchmarking frameworks have emerged to evaluate LLM agents under realistic scenarios. We summarize a few key ones and their intended uses:

Benchmark / Framework	Agent Type	Focus and Features	Example Tasks / Domains	Key Metrics
MultiAgentBench / MARBLE (2025)	Multi-agent	Comprehensive multi-agent scenarios (cooperative and competitive). Supports various coordination structures (star, chain, graph). Flexible planner strategies (CoT, group discussion, self-evolving).	Research collaboration, coding, gaming (e.g. multi-player puzzle, Werewolf game)¹¹	Task completion and milestone KPI, plus communication and planning scores (averaged into a Coordination Score)
Self-Evolving Benchmark (2024)	Single/multi	Dynamic benchmark that automatically generates new test instances. Uses a multi-agent “reframing” system to perturb or extend original data. Aims for robustness: adds noise, paraphrases, out-of-domain twists.	Extended QA, math, reasoning tasks (original datasets plus adversarial or rewritings)⁴	Original task accuracy plus performance drop on evolved instances (quantifies robustness). Fine-grained metrics for sub-abilities (e.g. change in chain-of-thought quality).
Domain Intelligence Benchmark Suite (DIBS) (2024)	Single-agent	Enterprise-focused tasks in finance, manufacturing, software. Emphasizes domain knowledge and tool use in real workflows. Sets defined subtasks with schemas (e.g. JSON schemas, API formats).	Text→JSON extraction, function-calling (API generation), and RAG workflows based on domain data (e.g. contracts, FAQs, SEC filings)¹²	Task-specific metrics: e.g., information extraction accuracy (F1/EM for JSON fields), function-call correctness (tool selection & JSON syntax), RAG answer quality (retrieval & answer F1)

The table above compares how these suites target different needs. For example, MultiAgentBench/MARBLE focuses on interactive teamwork: it measures milestone progress and coordination quality as described earlier. In contrast, DIBS is not about multiple agents at all but about single agents solving structured enterprise tasks. Its metrics (like JSON extraction F1 or exact-match on API calls) are more similar to traditional benchmarks, but with an enterprise twist. The Self-Evolving benchmark stands out by continuously creating harder test cases: here the key “metric” is often the gap between a model’s original accuracy and its accuracy on reframed instances. In practice, you would use Self-Evolving to see how quickly the agent’s performance degrades on slightly perturbed inputs.

Imagine testing a collaborative coding agent. Under MultiAgentBench, you might run the agent in a two-agent code review scenario. If the team finds 3 out of 5 bugs (60% task score) and completes 4/5 milestones, the KPI might be 80%. Meanwhile, the Communications Score might be low if agents exchanged few useful messages. By contrast, under Self-Evolving, you could reword those bug descriptions (“extra semicolon” → “optional punctuation”) and see if the agent still solves them; the drop in success rate reveals brittleness. Under DIBS, you could test a business assistant agent on extracting fields from an email into JSON – the score would be the percent of correctly filled fields.

Emerging Challenges in Agent Evaluation

Despite rapid progress, researchers face several challenges in evaluating LLM agents:

1) Lack of Standardization

Evaluation methods and metrics are still fragmented. For example, benchmarks often differ on how they define “task progress” or tool correctness, making cross-study comparison hard. The community is only beginning to agree on common schemes. To address this, consider using established frameworks (like those above) or follow community guidelines.

2) Scalability and Automation

Many evaluations rely on static datasets or human annotation (e.g. having people label success or judge responses). As agents become more capable, static tests quickly become outdated and we need to use synthetic data generation and “agent-as-judge” methods. In practice, you can automate test generation (e.g. using LLMs to rephrase queries) and even use an LLM to score agent outputs (with care). These approaches enable continuous, large-scale testing. For example, you might set up a pipeline where an LLM generates new test cases weekly and another LLM (or crowdsourced annotator) evaluates agent answers against them. Tools like LangSmith and AgentEvals already support LLM-as-judge workflows.

3) Diagnostic Tools (Failure Attribution)

Understanding why an agent fails is crucial but difficult. This problem is hard (even state-of-the-art models only ~50% accurate in tracing failures), but it underscores the need for better diagnostic metrics. In practice, log all agent steps, and consider developing automated monitors (e.g. check each step against subgoal criteria) to flag where breakdowns occur.

4) Safety and Fairness

Current benchmarks give little attention to safety, bias, or rule compliance. Emergent multi-agent behaviors could be harmful or unfair. Researchers are starting to create multi-dimensional safety tests – for example, evaluating agents on policy rules, adversarial prompts, or social norms. In the evaluations, include sanity checks: e.g. pose malicious tasks to ensure the agent refuses or handles them appropriately. For teams, watch for rude or manipulative communication – you might score dialogues for interactional fairness as a proxy for alignment.

5) Cost and Efficiency Metrics

As agents get bigger, resource use matters. Most evaluations ignore compute cost, focusing only on accuracy. We recommend tracking things like token usage, API calls, latency, and monetary costs alongside performance. For example, record how many total tokens are generated in multi-agent communication to complete a task. This cost-efficiency metric will help you balance capability against real-world constraints.

Best Practices for LLM Agent Evaluation

Based on current research and practical experience, here are actionable recommendations for researchers building or deploying LLM agents:

1. Define clear success and progress criteria: Early on, specify what counts as task completion and how to measure partial progress. Instrument the system to log intermediate steps or tool calls, so you can compute metrics like milestones achieved or action advancement. This makes evaluation quantitative instead of all-or-nothing.

2. Track tool usage in detail: Whenever an agent calls a tool (API, function, browser, etc.), log the chosen tool and parameters. During evaluation, compute selection accuracy and parameter accuracy by comparing these against expected calls. Also measure whether each tool call helped solve the task (e.g. by checking if the final answer changed from wrong to right). If the task allows, keep “gold” annotated tool paths and check agent compliance step-by-step.

3. Use layered metrics for team performance: For multi-agent systems, do not rely solely on final outcome. Record communication (number of messages, total tokens) and evaluate its quality (via LLM judges or rubrics). Calculate a Coordination Score as described above (averaging communication and planning ratings). Also split metrics per-agent where possible (e.g. each agent’s share of milestones). This helps identify if some agents are bottlenecks or if communication is too verbose.

4. Incorporate robustness testing: Don’t evaluate on one “clean” dataset only. Use data augmentation: paraphrase inputs, shuffle irrelevant facts, or inject noise. For example, follow the Self-Evolving approach and have an auxiliary agent generate new instances of the benchmark. Track how much performance drops under these variants. Significant drops indicate brittleness and should motivate further training or prompt tuning.

5. Include safety and alignment checks: Build in a suite of tests for undesirable behaviors. For instance, maintain a “red-teaming” set of prompts that try to elicit unsafe or biased outputs. Also evaluate neutrality: does the agent amplify stereotypes, or does one agent dominate a team? Use the notion of fairness in communication – e.g., have a separate LLM-rate each agent’s messages on respectfulness and clarity. Any failure in these audits should count as negative evidence in the overall evaluation.

6. Automate where possible: Leverage tools that make evaluation repeatable. For example, use open-source frameworks (like MARBLE, AgentSmith, or LangChain’s evaluation toolkit) to run scenarios and collect metrics. Generate synthetic tests (e.g. using LLMs to create prompts) to keep the test set fresh. Even consider using LLMs to serve as “committee judges” for easy tasks (with careful prompt engineering) to scale up evaluation throughput.

7. Report both mean metrics and distributions: Simple averages can hide failure modes. When publishing results, include metrics like success rate and variance in performance. For multi-agent trials, report things like “Agent A succeeded in 80% of trials versus Agent B’s 60%” to reveal imbalances. Plot progress over steps if possible (e.g. how quickly tasks are solved). Structured reports make it easier for others to compare methods.

8. Continuous benchmarking: As LLMs evolve, static tests become obsolete. Periodically revisit the evaluation suite. If possible, set up continuous integration that re-runs benchmarks (including newly generated cases) when you update the agent model. This way you catch regressions and improvements over time.

9. Balance breadth and focus: Use general benchmarks (like those above) for overall evaluation, but also craft domain-specific tests if you have particular use-cases. For example, if deploying a code assistant, include metrics for code correctness and execution coverage. If doing legal Q&A, measure citation accuracy. The DIBS suite illustrates matching benchmarks to enterprise needs.

By following these practices – tracking fine-grained progress, rigorously testing tools, auditing robustness/safety, and using shared benchmarks – researchers can more effectively evaluate and improve LLM agents. The combination of task performance, intermediate metrics, communication measures, and safety/fairness audits provides a full picture of agent capability. As the field matures, we anticipate more standardized tools and metrics (building on the frameworks above) that will make these evaluations easier and more comparable. For now, adopting a multi-faceted, systematic approach is the best way to ensure the LLM agent not only works, but works well and responsibly in practice.

Sources

AI, LLM, Agents, Evaluation

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