Top AI Agent Evaluation Tools in 2026

AI agents are reaching production, and evaluation hasn't kept up. Multi-step tool calls, MCP integrations, and autonomous decision-making create failure modes that traditional LLM evaluation (accuracy, relevance, toxicity) wasn't designed to catch. Evaluating agents requires assessing tool selection, trajectory quality, and whether outputs meet domain-specific standards that generic metrics cannot define.

The stakes are concrete. In March 2025, an AI agent at a fintech company entered a runaway loop during transaction reconciliation, running for 11 days and accumulating $47,000 in costs before anyone noticed. Gartner predicts over 40% of agentic AI projects will be cancelled by the end of 2027, often because teams lack evaluation infrastructure to catch failures before production.

We reviewed seven platforms that lead the agent evaluation space in 2026. They split into two camps: tools that trace agent execution (what the agent did) and tools that evaluate agent output quality (whether the agent did it well). The strongest evaluation strategies combine both. But if you have to choose one lens, output quality is what your users actually experience.

What makes agent evaluation different

Standard LLM evaluation checks whether a single response is relevant or factual. Agent evaluation adds layers of complexity:

• Multi-step decision chains where errors compound across tool calls and handoffs
• Tool selection and execution: did the agent pick the right tool with the right arguments?
• Trajectory efficiency: even when the final answer is correct, did the agent take an unnecessarily expensive or slow path?
• Domain-specific output quality: generic metrics can't assess whether a medical agent's triage was clinically sound or a legal agent's brief was jurisdictionally accurate

Most platforms on this list address the first three well. Domain-specific quality is where evaluation gaps persist.

Platform breakdowns

1. Truesight

Truesight evaluates whether agent outputs meet domain-specific quality standards defined by your experts, not generic metrics. The platform is purpose-built for evaluation, not tracing. Where other tools instrument what agents did, Truesight answers the most important question: did the agent produce the right result for your use case?

• Guided, no-code setup for non-technical domain experts
• Live API endpoints deploy evaluations to production
• Systematic error analysis to discover evaluation criteria from data
• Multi-model support: OpenAI, Anthropic, Google, any LiteLLM provider
• SME review queue for flagging and auditing edge cases

Best for: Teams where domain-specific output quality is the primary concern: healthcare, education, finance, legal.

2. Weights & Biases Weave

The strongest agent observability layer available. MCP auto-logging requires a single @weave.op() decorator, and guardrails provide real-time agent behavior controls. Now under CoreWeave following the 2025 acquisition.

• MCP tracing support for client and server operations
• Local SLM scorers run without API calls for lower-cost evaluation
• Integrations: OpenAI Agents SDK, CrewAI, PydanticAI, LangGraph, Google ADK
• Online Evaluations for real-time production monitoring
• Pre-built scorers for hallucination, context relevance, coherence, toxicity

Best for: Teams that need comprehensive agent tracing with deep framework integrations.

3. LangSmith

The default for LangChain and LangGraph teams. Multi-turn evaluations score complete agent conversations on semantic intent and trajectory. Per-seat pricing at $39/month and LangChain-ecosystem bias are the main trade-offs.

• Multi-turn agent conversation evaluation with trajectory scoring
• Insights Agent (GA October 2025) clusters production traces to surface failure patterns
• Fetch CLI pipes trace data to coding agents like Cursor and Claude Code
• Annotation queues for structured human review at scale
• Automatic LangGraph step-by-step execution visualization

Best for: Teams already invested in the LangChain/LangGraph ecosystem.

4. Arize Phoenix

The only major platform built entirely on OpenTelemetry with no proprietary tracing layer. Fully self-hostable with zero feature gates, which is uncommon in this space. Licensed under Elastic License 2.0.

• 4 dedicated agent evaluators: function calling, path convergence, planning, reflection
• MCP tracing via the open-source OpenInference specification
• Auto-instrumentation for CrewAI, LangGraph, AutoGen, Agno, smolagents
• Free self-hosting on Docker, Kubernetes, or AWS CloudFormation
• Alyx AI copilot for trace troubleshooting and prompt optimization (AX tier)

Best for: Teams that need vendor-agnostic, OTel-native observability with free self-hosting.

5. Comet Opik

Opik's standout is the Agent Optimizer: six algorithms that automatically refine prompts and tool configurations for agentic systems. Apache 2.0 licensed. At $19/month, the cheapest paid tier on this list.

• Agent Optimizer: Bayesian, evolutionary, MetaPrompt, GEPA, and more
• 40+ framework integrations, including Google ADK, LiveKit Agents, Strands Agents
• Built for high-volume trace ingestion at production scale
• Online Evaluation Rules with LLM-as-judge for real-time issue detection
• OpikAssist AI debugging for natural-language root-cause analysis

Best for: Teams focused on automated agent optimization at scale.

6. Braintrust

Strongest CI/CD story with ease of understanding which test cases improved or regressed. GitHub Actions posts eval results on every PR. The $249/month entry price and enterprise-only self-hosting are the main constraints.

• Loop AI agent automates prompt, scorer, and dataset optimization
• Brainstore: purpose-built database claiming 80x faster trace analytics
• 8 RAG-specific scorers out of the box
• Side-by-side experiment diffs with per-test-case regression tracking
• Per-organization pricing with unlimited users on all tiers

Best for: Engineering teams that want eval integrated directly into their CI/CD pipeline.

7. DeepEval by Confident AI

The most metrics-dense option: 50+ built-in metrics, including six agent-specific ones. The DAG metric offers deterministic multi-step scoring that avoids LLM-judge non-determinism. Python-only, and most metrics require LLM API calls, which adds cost at scale.

• 6 agent metrics: task completion, tool correctness, step efficiency, plan adherence, plan quality, argument correctness
• DAG metric: deterministic decision-tree scoring without LLM non-determinism
• Native Pytest integration for CI/CD pipeline evaluation
• Synthetic data generation including multi-turn conversational scenarios
• Red teaming scans for 40+ safety vulnerabilities

Best for: Python teams that want the broadest set of off-the-shelf agent evaluation metrics.

How to choose

The right tool depends on what you're optimizing for. Here's a quick decision framework:

• Choose Truesight when domain experts (doctors, teachers, lawyers, analysts) need to define what “good” means and output quality matters more than execution tracing.
• Choose W&B Weave when you need deep agent trace observability with MCP auto-logging across multiple frameworks.
• Choose LangSmith when your stack is built on LangChain or LangGraph and you need annotation queues for human review at scale.
• Choose Arize Phoenix when vendor-neutral, OTel-native instrumentation and free self-hosting are requirements.
• Choose Comet Opik when automated prompt and tool optimization is the priority and you need the broadest framework ecosystem.
• Choose Braintrust when eval results need to gate your CI/CD pipeline and you want AI-automated prompt optimization.
• Choose DeepEval when you need the broadest set of off-the-shelf metrics and Pytest is already your testing framework.

Disclosure: Truesight is built by Goodeye Labs, the publisher of this article. We've aimed to provide a fair and accurate comparison based on each platform's documented capabilities as of February 2026.