Galileo announced the launch of its comprehensive platform update for AI agent reliability, free for developers around the world.
As AI agents become increasingly autonomous and multi-step, traditional evaluation tools struggle to detect their complex failure modes. Galileo's new agent reliability solution is purpose-built for multi-agent AI systems and addresses this critical gap with agentic observability, evaluation, and guardrail capabilities working in concert.
Galileo's platform addresses the high-stakes nature of enterprise AI deployment, where a single agent failure can expose sensitive data, cost real money, or damage customer relationships. Galileo's new Luna-2 small language models(SLMs) deliver up to 97% cost reduction in production monitoring while enabling real-time protection against failures that could derail enterprise AI initiatives.
"When your agent fails, you shouldn't have to become a detective," said Vikram Chatterji, CEO and Co-founder of Galileo. "Our agent reliability platform, fueled by our world-first Insights Engine, represents a fundamental shift from reactive debugging to proactive intelligence, giving developers the confidence to deploy AI agents that perform reliably in production."
The platform tackles the unique challenges of agentic AI development, where a single bad action can expose sensitive data or cost real money, requiring guardrails that trigger before tools execute. Galileo's platform powers custom real-time evaluations and guardrails with new Luna-2 small language models, giving developers targeted visibility into agent behavior across every step, tool call, and output.
Galileo's Agent Reliability Platform delivers four key capabilities:
1. Agent Observability Reimagined
- Framework-agnostic Graph Engine that renders every branch, decision, and tool call
- Timeline View for execution flow analysis and bottleneck identification
- Conversation View for user-perspective debugging
2. Insights Engine for Automatic Failure Detection Powered by bespoke evaluation reasoning models, the Insights Engine automatically identifies failure modes and surfaces actionable insights, including:
- Root cause analysis linking errors to exact traces
- Multi-agent coordination analysis
- Tool usage optimization recommendations
- Conversation flow and performance monitoring
3. Scalable Agentic Metrics Purpose-built metrics covering flow adherence, task completion, conversation quality, and agent efficiency, with support for custom metrics using code-based approaches, LLM-as-a-judge, or Galileo's new Luna-2 small language models.
4. Real-Time Production Guardrails Luna-2 powered guardrails enable low-cost, real-time protection against malicious user behavior and agent mistakes without the expense of traditional LLM-based solutions.
Central to the platform are Galileo's Luna-2 small language models, specifically designed for always-on agent evaluations. Unlike traditional approaches that rely on expensive, slow LLMs, Luna-2 enables:
- 10-20 sophisticated metrics running simultaneously
- Sub-200ms latency even at 100% sampling rates
- Enterprise-scale production monitoring at 97% cheaper costs
- Session-level metrics that capture the entire agent journey
"Multiturn agents never follow a single script, so your tests can't either," explained Atin Sanyal, CTO and Co-founder of Galileo. "Luna-2's session metrics capture conversation quality, intent changes, efficiency, and compound-request resolution across the whole journey, not just individual turns."
The Galileo Agent Reliability Platform is available now as part of Galileo's free tier, with additional enterprise features available through paid plans. The platform integrates with popular agent frameworks, including CrewAI, LangGraph, OpenAI's Agent SDK, LlamaIndex, and Amazon Strands, leveraging open standards like OpenTelemetry for maximum compatibility.
To accompany the platform, Galileo has also released a new v2 of its viral AI agent leaderboard today. The leaderboard evaluates models for their effectiveness in solving domain-specific enterprise tasks across different purpose-built agent metrics and datasets covering banking, healthcare, insurance, investments, and telecoms. OpenAI's GPT-4.1 tops the updated research, and Kimi K2 leads among open-source models.
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