Introducing Galileo S Agent Reliability Platform

Bonisiwe Shabane

-Jan 27, 2026, 4:18 AM

introducing galileo s agent reliability platform

Pioneering AI evaluation company introduces industry-first platform combining observability, evaluation, and guardrails specifically designed for multi-agent systems SAN FRANCISCO, July 17, 2025 /PRNewswire/ -- Galileo, the leading AI reliability platform trusted for evaluations and observability by global enterprises including HP, Twilio, Reddit, and Comcast, today announced the launch of its comprehensive... 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. With 10% of organizations already deploying AI agents and 82% planning integration within three years, enterprises face a critical challenge: ensuring reliable AI agent performance at scale. 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." Enterprise customers and partners are already seeing a significant impact: 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. Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: 🔎 Observability redesigned for agents Trace... This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail.

🔁 Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. 📊 Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more.

For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. ⚡ Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. 🆕 Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation.

Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇 #AgenticAI #AIObservability #AIInfrastructure #LLMOps #GalileoAI #ReliableAI So excited this is live! Great job on the video Brent Barrie & Jackson Wells + fantastic job Vikram Chatterji! What a team effort here!

So much work went into this release, and I can't wait to see how it helps teams ship more reliable agents at scale 💪 Agentic AI: from “web pages” to “work protocols” In 1995, most companies launched a website and called it transformation. The winners didn’t make prettier pages—they rewired flows: inventory ↔ payments ↔ logistics. The internet stopped being a brochure and became infrastructure. Agentic AI is that moment again. Not “a smarter chatbot,” but a fabric of software teammates that pursue goals, use your tools, follow guardrails, and leave evidence.

Think less homepage, more shipping API. A simple model executives can use: The Work Graph. Every org has one—people, queues, apps, data, decisions. Agentic systems traverse that graph on purpose. Four practical laws (no mysticism required): 1. Goal > Model.

Start with the business objective and success metric—then choose models. 2. Tools > Talk. Agents must read/write the systems where work actually lives. 3. Guardrails > Good intentions.

Policies, permissions, and thresholds define where autonomy is safe. 4. Evidence > Opinion. If it isn’t logged end-to-end, it didn’t happen. What changes (and why it matters): • Flow over fragments. We stop optimizing steps in isolation and optimize the handoffs.

• Throughput over headcount. The primary lever becomes queue time and exception rate, not meetings. • Resilience by design. Agents detect drift, retry safely, and escalate with context—like a circuit breaker for operations. • Know-how compounds. Each resolved case teaches the next one; your operating playbook gets encoded and reused.

How to start without boiling the ocean: • Pick one lane (e.g., intake→decision→booked or exception→evidence→fix). • Define the source of truth, decision rules, and when humans must approve. • Run with evidence on: before → during → after examples, tied to a single KPI (cycle time, touches, first-pass yield). • Grant a small autonomy budget (where it’s safe), expand only when the logs say it’s working. • Treat agents like any other system component—versioned, tested, and observable. Bottom line: In the web era, advantage came from turning interfaces into protocols.

In this era, it comes from turning tasks into flows that run themselves—safely, measurably, and under your governance. The companies that master the work graph won’t look flashy; they’ll just move faster, with fewer errors, week after week. The four-criteria framework for using agents Before you build or deploy an AI agent, ask yourself the following questions: 1. Is the task ambiguous or predictable? * Use agents when the task is ambiguous: - The decision path is unclear or cannot be mapped in advance - Tasks involve exploration, troubleshooting, or creativity * Use workflows when the task is... Is the value of the task worth the cost?

AI agents are more expensive to operate due to exploration overhead. They can consume 10 to 100× more tokens than a workflow. Let's look at some scenarios: Scenario -> Recommendation Strategic planning with high ROI -> Use an agent Basic customer support task -> Use a workflow instead 3. Does the agent meet minimum capabilities? Before launch, test the agent on three to five key skills. Here are some examples: - A research agent must identify, filter, and summarize credible sources - A coding agent must write, fix, and validate code snippets - A customer support agent must classify issues,...

4. What happens if the agent makes a mistake? Evaluate the answers to these questions - Can you catch and correct errors quickly? If so, then using an agent might be appropriate. - What's the risk if something is missed? Does the consequence of missing the answer affect the customer’s or organization’s well-being or safety?

- Does the agent include built-in correction or validation tools? - Use agents when risk is manageable or reversible. Galileo, a leading AI evaluation company based in San Francisco, announced the launch of its Agent Reliability Platform on July 17, 2025. This industry-first solution, available free for developers worldwide, combines observability, evaluation, and guardrails tailored for multi-agent AI systems. Trusted by global enterprises like HP, Twilio, Reddit, and Comcast, the platform addresses the critical need for reliable AI agent performance as adoption surges, with 10% of organizations already using AI agents and 82%... Powered by Galileo’s Luna-2 small language models, it offers scalable, cost-effective tools to ensure robust AI deployments.

Introducing Galileo S Agent Reliability Platform

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