[Galileo is now part of Cisco](https://blogs.cisco.com/news/Cisco-announces-the-intent-to-acquire-galileo) Mar 16, 2026 # 5 Tools to Evaluate and Monitor Multi-Agent AI Systems **TLDR:** - Multi-agent systems [fail at 41-86.7% rates](https://arxiv.org/pdf/2503.13657) according to ArXiv studies without proper evals infrastructure - Coordination overhead consumes 4-15x more tokens than single-agent systems - Quality issues [represent the number one barrier](https://www.langchain.com/state-of-agent-engineering) to production deployment - Purpose-built platforms measure Tool Selection Accuracy and Agent Adherence - Leading platforms combine observability, runtime protection, and automated root cause analysis ## **What are multi-agent AI evaluation platforms?** Multi-agent AI evaluation platforms are specialized observability systems designed to monitor and improve autonomous agent reasoning, reliability, and performance. These platforms instrument the decision-making layer—capturing agent reasoning, tool selection, and inter-agent communication patterns. ## **1 . Galileo** Galileo provides a comprehensive [agent observability](/content/blog/ai-agent-observability/index.html) platform specifically built for multi-agent systems, combining real-time evals, automated failure detection, and runtime protection. ### **Key features** - Automated failure clustering through the Insights Engine that identifies root causes through intelligent pattern recognition, grouping related failures within minutes - [Luna-2 small language model](/content/luna-2/index.html) family providing purpose-built assessment capabilities optimized for evaluation tasks with faster inference and lower costs than GPT-4 - Evaluation-specific architecture optimizing for consistency across quality dimensions, including context adherence, instruction following, completeness, and chunk attribution - Runtime protection through Galileo Protect with real-time [guardrails](/content/blog/ai-guardrails-framework/index.html) blocking unsafe outputs before user impact - PII detection and redaction, prompt injection prevention, jailbreak prevention, toxicity filtering, and hallucination detection - Native integration with major frameworks, including OpenAI Agents SDK, LangChain, LlamaIndex, and CrewAI through environment-variable configuration ### **Strengths and weaknesses** **Strengths** - Automated failure pattern detection reduces debugging time from hours to minutes - Luna-2 models make continuous evaluation economically viable at the enterprise scale - Hierarchical trace visualization maps complex multi-agent decision flows - Framework-agnostic integration requiring minimal code instrumentation **Weaknesses** - Integration complexity varies by framework—LangChain needs only environment variables while CrewAI demands comprehensive instrumentation ### **Use cases** JPMorgan Chase improved domain-specific query accuracy using Galileo's multi-agent AI governance—eliminating a backlog of 1 million customer utterances without manual review. ## **2. Arize Phoenix** Phoenix's distributed tracing reveals where agent-to-agent handoffs actually fail through the CLEAR framework: Cost, Latency, Efficacy, Assurance, and Reliability. ### **Key features** - Distributed tracing capturing agent-to-agent communication patterns with granular visibility into agent interaction quality - CLEAR framework metrics: Cost, Latency, Efficacy, Assurance, and Reliability - Drift detection monitoring that tracks both performance degradation and behavioral changes over time - Alerts when agent decision patterns shift unexpectedly before customer impact occurs - Open architecture enabling custom metric development for organizations with specific coordination measurement requirements - OpenTelemetry compatibility integrating Phoenix into existing observability stacks ### **Strengths and weaknesses** **Strengths** - Open-source architecture enables extensive customization while maintaining comprehensive CLEAR framework coordination metrics - Drift detection capabilities catch behavioral changes before customer impact occurs, providing early warning systems for production deployments - OpenTelemetry compatibility connects agent-level metrics with infrastructure performance data **Weaknesses** - Custom metric development requires technical expertise beyond simple configuration approaches - Distributed tracing setup involves more complex implementation than environment-variable-based alternatives ### **Use cases** Teams deploy Phoenix when coordination-specific metrics matter more than general observability—particularly when drift detection can catch degradation before customers experience failures. ## **3. LangSmith** LangSmith, built by the LangChain team, provides native observability and evaluation infrastructure purpose-built for agent engineering workflows. ### Key features - Insights Agent that automatically analyzes production traces to discover and surface common usage patterns, agent behaviors, and failure modes across thousands of interactions - Multi-turn Evals measuring whether agents accomplish user goals across entire conversations, not just individual steps—assessing semantic intent, goal completion, and interaction quality - Environment-variable setup requiring only LANGSMITH_TRACING=true and API credentials for comprehensive trace collection without code instrumentation - Online and offline evaluation modes—offline evals run against datasets for benchmarking and regression testing while online evals run on real production traffic in near real-time - Annotation queues for collecting expert feedback, flagging runs for review, and using human input to improve prompts, evaluators, and datasets - OpenTelemetry compatibility enabling integration with existing observability pipelines - Production monitoring dashboards tracking token usage, latency (P50, P99), error rates, cost breakdowns, and feedback scores with configurable alerts via webhooks or PagerDuty ### Strengths and weaknesses **Strengths** - Native LangChain and LangGraph integration delivers zero-configuration observability with automatic capture of chains, tools, and retriever operations - Insights Agent automates pattern discovery across production traces - Multi-turn Evals close the gap between individual trace evaluation and holistic conversation quality assessment - Framework-agnostic support through OpenTelemetry means LangSmith works with OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex **Weaknesses** - Deepest integration experience requires LangChain or LangGraph - LangSmith operates as a paid service (Plus and Enterprise tiers) beyond the free developer tier ### Use cases Teams deploy LangSmith when they need full visibility into multi-turn agent behavior at scale. ## **4. Braintrust** Braintrust integrates evaluation directly into observability, measuring how well agents perform using customizable metrics rather than just logging what happened. ### Key features - Loop AI agent that automates the most time-intensive parts of AI development—analyzing prompts, generating better-performing versions, creating evaluation datasets, and building custom scorers tailored to specific use cases - Brainstore, a purpose-built database for AI application logs delivering 80x faster query performance than traditional databases - Comprehensive trace capture showing every decision point in multi-step workflows - One-click production trace conversion into evaluation datasets - Native CI/CD integration through GitHub Actions and CircleCI ### Strengths and weaknesses **Strengths** - Evaluation-first architecture means teams catch regressions before customers see them - Loop AI agent reduces the tedious work of writing custom scorers - Brainstore enables debugging at production scale **Weaknesses** - Enterprise pricing lacks self-serve options - Platform depth in evaluation and observability may exceed what teams need if they're looking for simple logging ### Use cases Teams adopting Braintrust report transformative improvements in debugging velocity and output quality. ## **5. LangChain** LangChain's open-source foundation provides flexibility through supervisor-worker patterns and comprehensive logging. ### **Key features** - Environment-variable approach delivering OpenTelemetry-compatible observability infrastructure - Supervisor-worker coordination patterns proven across thousands of implementations - Error handling frameworks capturing failure context in detailed logs - Distributed tracing tracking agent-to-agent communications throughout complex workflows ### **Strengths and weaknesses** **Strengths** - Open-source foundation avoids vendor lock-in while providing production-tested coordination patterns - Environment-variable setup enables rapid instrumentation without significant code modifications **Weaknesses** - LangSmith observability operates as a separate paid service ### **Use cases** Teams deploy LangChain when building custom agent architectures requiring specialized coordination logic unavailable in managed platforms. ## **Choose the Right Platform to Prevent Multi-Agent Failures** McKinsey research shows most companies using generative AI report minimal bottom-line impact, largely due to inadequate evaluation infrastructure. While each platform offers unique strengths—Phoenix for drift detection, Maxim for pre-production simulation, LangChain for open-source flexibility—Galileo stands out as the most comprehensive solution. Here’s how Galileo helps evaluate multi-agent AI systems: - **Automated root cause analysis** — The Insights Engine clusters similar failures across agent executions - **Purpose-built evaluation models** — Luna-2 delivers faster inference and lower costs than GPT-4 - **Real-time guardrails** — Galileo Protect blocks PII leakage, prompt injection, jailbreaks, and hallucinations before they impact users - **Hierarchical trace visualization** — Maps multi-agent decision flows from orchestrator to worker agents - **Framework-agnostic integration** — Native support for OpenAI Agents SDK, LangChain, LlamaIndex, and CrewAI