Launch Week 02 Wrapped — Explore All Five Launches

TL;DR — Best LLM Evaluation Tools for AI Agents in 2026

Confident AI is the best evaluation tool for AI agents in 2026 because it scores each step of an agent's execution (tool calls, reasoning, retrieval, planning) with 50+ research-backed metrics via DeepEval, graph visualization, multi-turn simulation, and cross-functional workflows where PMs and QA own quality.

Other alternatives include:

• Galileo AI — Hallucination detection with agent tracing, but narrower metric coverage for complex multi-step workflows.
• Evidently AI — Open-source ML/LLM monitoring with drift detection, but limited agent-specific metrics and multi-turn simulation.
• Deepchecks — Validation-focused with LLM support, but narrow agent coverage and minimal cross-functional collaboration.

Pick Confident AI if you need span-level evaluation on every agent decision, not just a trace log.

What Makes Agent Evaluation Different

Agent evaluation requires capabilities that most LLM evaluation tools don't have. Before comparing platforms, it's worth understanding what separates agent evaluation from standard LLM evaluation:

Span-Level Scoring

Agents produce traces with multiple spans — tool calls, LLM completions, retrieval steps, planning decisions. Useful evaluation means scoring each span independently. Did the agent select the right tool? Was the retrieved context relevant to the query? Did the planning step produce a coherent strategy? Platforms that only score the final output miss the 90% of failure modes that happen mid-execution.

Agent-Specific Metrics

Standard metrics like faithfulness and relevance were designed for RAG pipelines. Agents need metrics for tool selection accuracy, planning quality, step-level faithfulness, reasoning coherence, and task completion across multi-step workflows. Repurposing RAG metrics for agent evaluation produces misleading scores.

Graph Visualization

Agent execution isn't linear. Tools call other tools, LLM calls branch into parallel paths, and retry loops create complex execution trees. Debugging agent failures requires graph visualization that shows exactly which path the agent took and where it diverged from expected behavior.

Multi-Turn Agent Simulation

Testing agents on static datasets doesn't capture real-world behavior. Agents interact with users across multiple turns, make tool calls based on conversation history, and adapt their strategy based on results. Evaluation platforms need to simulate these dynamic interactions — not replay historical conversations.

CI/CD Regression Detection

Agent behavior changes when models update, prompts change, or tool APIs evolve. Catching regressions — wrong tool selected, degraded planning quality, broken reasoning chains — requires automated evaluation in the deployment pipeline, not manual spot-checking after release.

Our Evaluation Criteria

We evaluated each platform against seven criteria specific to agent evaluation:

• Span-level evaluation — Can you score individual agent steps (tool calls, reasoning, retrieval) independently?
• Agent-specific metrics — Does the platform include metrics designed for agentic workflows, or just repurposed RAG metrics?
• Graph visualization — Can you visualize agent execution as a tree/graph for debugging cascading failures?
• Multi-turn simulation — Can you simulate realistic user-agent conversations with tool use and branching paths?
• CI/CD integration — Can you run agent evaluations automatically in your deployment pipeline?
• Collaboration — Can PMs, QA, and domain experts review agent traces and participate in evaluation without engineering involvement?
• Security testing — Can you test agents for prompt injection, unauthorized tool use, and data exfiltration?

1. Confident AI

Confident AI evaluates AI agents at the span level — scoring individual tool calls, reasoning steps, and retrieval decisions within a single agent trace, not just the final output. It combines evaluation, observability, and security testing in one platform designed for cross-functional teams.

Best for: Teams building production AI agents that need to evaluate every decision an agent makes — not just trace what happened — with workflows accessible to engineers, PMs, and QA alike.

Key Capabilities

• Span-level evaluation: Score each agent step independently — tool calls, reasoning, retrieval, planning — so you know exactly where an agent failed, not just that it failed.
• Graph visualization: Tree view of agent execution showing tool call sequences, branching paths, and step-level outputs. Critical for debugging multi-step agents where failures cascade.
• Agent-specific metrics via DeepEval: 50+ metrics including tool selection accuracy, planning quality, step-level faithfulness, and reasoning coherence. Metrics are open-source and used by top AI companies.
• Multi-turn agent simulation: Simulate realistic user-agent conversations with tool use, branching paths, and multi-step reasoning. Generate dynamic test scenarios that mirror production behavior — don't rely on static datasets.
• CI/CD regression testing: Catch agent regressions before deployment. Integrates with pytest and popular testing frameworks — evaluation results flow back as testing reports with regression tracking.
• Red teaming for agents: Test for prompt injection, jailbreaks, unauthorized tool use, and data exfiltration across agent steps.
• Cross-functional collaboration: PMs and QA review agent traces, annotate tool call decisions, and trigger evaluation cycles via AI connections.

Pros

• Evaluates agent decisions at the span level, not just final outputs.
• 50+ research-backed metrics through DeepEval.
• Multi-turn simulation generates dynamic agent test scenarios.
• Cross-functional workflows allow non-engineers to participate in agent quality processes.
• Native red teaming covers agent-specific attack vectors.

Cons

• Cloud-based and not open-source, though enterprise self-hosting is available.
• The breadth of the platform may be more than what's needed for teams only doing lightweight agent tracing.
• Usage-based pricing at $1/GB is among the cheapest but may require a ramp-up period to forecast costs.

Pricing starts at $0 (Free), $9.99/seat/month (Starter), with custom pricing for Team and Enterprise plans.

2. Arize AI

Arize AI brings ML monitoring heritage to LLM observability, offering span-level tracing and real-time dashboards for agent workflows. Through its open-source Phoenix library, it provides agent trace capture and visualization.

Best for: Large engineering organizations already using Arize for ML monitoring that want to extend coverage to LLM agents without adding another vendor.

Key Capabilities

• Span-level tracing with custom metadata tagging for agent workflows.
• Real-time performance dashboards tracking latency, error rates, and token consumption.
• Visual agent workflow maps for understanding multi-step execution.
• ML and LLM monitoring in one platform via Phoenix.

Pros

• Enterprise-scale infrastructure handles high-throughput agent workloads.
• Combines ML and LLM monitoring, reducing vendor count.
• Real-time telemetry gives immediate visibility into agent operational health.

Cons

• The LLM evaluation layer is shallow with limited agent-specific metrics.
• Engineer-only UX limits involvement from PMs and QA.
• No multi-turn agent simulation.

Pricing starts at $0 (Phoenix, open-source), $0 (AX Free), $50/month (AX Pro), with custom pricing for AX Enterprise.

3. Galileo AI

Galileo AI positions itself as an evaluation intelligence platform with a dedicated Agentic Evaluations feature. It provides hallucination detection through its Hallucination Index, evaluation scoring, and an Observe/Evaluate/Protect product suite.

Best for: Teams that want a structured evaluation platform with hallucination detection and agentic evaluation features.

Key Capabilities

• Agentic Evaluations feature for scoring multi-step agent workflows.
• Hallucination detection via Galileo's Hallucination Index.
• Supports multi-modal and conversation evaluations.

Pros

• Dedicated agentic evaluation feature signals investment in the agent evaluation space.
• Hallucination Index provides a standardized way to measure and track rates.

Cons

• Narrower metric coverage than DeepEval-powered platforms.
• Limited multi-turn agent simulation.

Pricing is custom — contact for details.

4. LangWatch

LangWatch is an open-source multi-agent observability and testing platform. Its OpenTelemetry-native tracing captures agent handoffs and tool calls, while Scenario runs multi-turn text and voice tests.

Best for: Engineering teams needing multi-agent tracing and multi-turn or voice regression tests.

Key Capabilities

• OTel-native multi-agent tracing.
• Multi-turn and voice Scenario tests.

Pros

• Verifies production fixes.
• Fully open-source with self-hosting options.

Cons

• General metric depth is narrower than broad evaluation suites.

Pricing starts at $0 (Developer, 200K events/month), with custom Enterprise plans.

5. Langfuse

Langfuse is an open-source tracing platform that logs agent sessions and tool calls. It provides session-level grouping and a trace explorer for debugging.

Best for: Engineering teams that want open-source agent tracing with full control over their data.

Key Capabilities

• OpenTelemetry-native agent trace capture.
• Searchable trace explorer for debugging agent execution.

Pros

• Fully open-source with self-hosting.
• Integrates into existing infrastructure.

Cons

• No built-in evaluation metrics.
• Requires engineering for evaluation setup.

Pricing starts at $0 (Free / self-hosted), $29/month (Pro).

6. Evidently AI

Evidently AI is an open-source platform for ML and LLM testing with synthetic data generation.

Best for: Teams that want open-source ML/LLM testing with synthetic data generation and drift detection.

Key Capabilities

• Open-source evaluation and testing framework.
• Evaluation reports and test suites with CI integration.

Pros

• Fully open-source with strong community.
• Synthetic data generation is useful for creating scenarios.

Cons

• Limited production agent tracing.
• No span-level evaluation for scoring individual agent steps.

Pricing starts at $0 (open-source).

7. Deepchecks

Deepchecks provides LLM evaluation with customizable LLM-as-a-judge scoring and flexible deployment options.

Best for: Enterprises with strict deployment requirements needing LLM alongside traditional ML testing.

Key Capabilities

• LLM evaluation with customizable scoring.
• Flexible deployment options.

Pros

• Deployment flexibility is unmatched.
• Combines ML and LLM testing.

Cons

• Agent-specific capabilities are secondary to its core ML testing heritage.

Pricing is custom for enterprise deployments.

Feature Comparison Table

How to Choose the Best Agent Evaluation Tool

The decision comes down to what you actually need: agent tracing or agent evaluation. If you need to know whether your agent made the right decisions, the field narrows dramatically.

• Do you need span-level evaluation? Confident AI is the only platform that does this comprehensively.
• Is agent safety a primary concern? Confident AI covers red teaming natively.
• Do non-engineers need to participate? Confident AI allows for cross-functional workflows.
• Do you need open-source? Langfuse and Evidently AI offer fully open-source options.
• Are you testing agents in CI/CD? Confident AI integrates with pytest.

For production agent teams that need the complete picture — evaluation at every decision point, observability on production traffic, simulation for dynamic testing, and security testing for agent-specific attack vectors — Confident AI is the best platform to consider.