Jeetu Patel's announcement of Cisco's intent to acquire Galileo included a line that cuts straight to the point: "The observability problem and the security problem are the same problem." That is not a vision statement. It is a diagnosis of how most enterprises are currently failing at both.

Where Galileo Came From

Vikram Chatterji, Atindriyo Sanyal, and Yash Sheth founded Galileo in 2021. They were not academics who had studied the AI reliability problem from a distance. Chatterji had been running a product management team at Google AI, working on large language models that processed unstructured financial documents. The data preparation work, the cleaning, the error-checking, the bias hunting, absorbed most of his team's time and produced inconsistent results anyway. Sanyal had led engineering at Uber AI's Michelangelo platform, co-architecting the feature store that powered data quality across more than a thousand production models as Uber scaled. He had also been an early member of the Siri team at Apple. Sheth ran the Google Speech Recognizer platform, managing the infrastructure that powered speech recognition across more than 20 consumer products and thousands of enterprise deployments globally. The three of them had, between them, shipped production AI at a scale very few teams ever reach, and they had all hit the same wall: building a high-quality model was not the hard part. Knowing whether your data was trustworthy enough to build it on, and whether the deployed model was still behaving correctly in production, was the hard part and almost entirely manual.

Galileo came out of stealth in May 2022 with $5.1 million in seed funding, initially positioning as a machine learning data intelligence platform for unstructured data. The original product helped data scientists surface erroneous or underrepresented data cohorts, the kind of work that was eating weeks of engineering time at companies that were scaling ML fast. That framing did not last long. As large language models went from research curiosity to enterprise infrastructure between 2022 and 2023, the relevant failure mode shifted from bad training data to unpredictable inference behavior. An LLM does not fail the way a deterministic system fails. It drifts. It hallucinates. It produces outputs that are plausible enough to pass a surface review and wrong enough to cause real damage at scale. Galileo pivoted to that problem, building what Sheth called a "trust layer" centered on evaluation intelligence: the right metrics and infrastructure to measure holistically how AI applications are actually performing, continuously, in production.

Two Teams, One Blind Spot

Go into almost any large organization running AI agents today and you will find the same setup. Security has a toolchain watching for threats. Operations has a separate toolchain watching performance, cost, and quality. They share almost no telemetry. Neither team has the full picture. An agent being slowly manipulated through prompt injection will surface as a latency anomaly before it reads as a security event, by which point it has already been doing damage for days.

Patel made the case at RSA Conference 2026 that the agentic workforce needs the same governance concepts we built for human workers: identity, background checks, policy enforcement. Galileo applies that same logic one layer up, to the question of whether an agent is actually doing what it was trained to do. Latency metrics, the traditional operations signal, tell you nothing about behavioral drift, token waste, or outputs that create compliance exposure. You need quality signals, cost signals, behavioral patterns, and security indicators together, in real time, from the same system.

What Galileo Actually Built

Galileo's platform centers on Luna-2, a family of small language models purpose-built for evaluation. Galileo advertises these models running more than 20 quality and safety checks simultaneously at under 200ms latency, fast enough to operate as production guardrails without adding meaningful overhead to inference. The same evaluation metrics used during pre-production testing get promoted directly into production monitoring. One framework, one set of policies, full lifecycle coverage.

Generic evaluation models commonly plateau around 70% F1 scores on real enterprise data, according to vendor and practitioner reports. Domain terminology, regulatory edge cases, and organization-specific reasoning patterns sit outside what a general-purpose model was trained to catch. Galileo fine-tunes Luna-2 against an organization's own production feedback over time. The guardrail gets more accurate as it learns the deployment. That is switching cost built into the product, and architecturally it creates the kind of embedded institutional knowledge that could deepen the connection between an enterprise's AI stack and Splunk as the integration matures.

There is no public indication of financial distress at Galileo. A $45 million Series B closed in October 2024, 834% revenue growth reported since the start of that year, Fortune 500 enterprise customers including Comcast and Twilio. Strategic investors included Databricks Ventures, ServiceNow Ventures, and SentinelOne Ventures. That last one is worth noting: SentinelOne is a security vendor. Galileo's investor table already reflected the thesis that evaluation infrastructure and security infrastructure belong together.

The Splunk Integration Is the Whole Bet

Cisco has stated that Galileo's capabilities will be integrated into AI Agent Monitoring in Splunk Observability Cloud. That is the plan on paper. Whether the integration runs deep or ends up as a dashboard tab is the only question that matters for enterprise buyers. Cisco absorbed Splunk less than two years ago and is still building out that platform. Now it is adding another acquisition on top, with a team that built something architecturally distinct from traditional observability tooling.

Founders Vikram Chatterji, Atindriyo Sanyal, and Yash Sheth spent years building on the conviction that evaluation infrastructure has to be purpose-built. It cannot be retrofitted from generic monitoring tools. If Cisco's integration preserves that architecture and exposes it properly through Splunk's data plane, the result is genuinely differentiated: a platform where behavioral evaluation and security telemetry inform each other. If the integration is shallow, the acquisition delivered talent, not capability.

One More Thread in the Clawconomy Stack

Cisco shipped DefenseClaw as open source at RSA Conference 2026, framed as a vulnerability scanner for AI agents running across OpenClaw-compatible infrastructure. Galileo released Agent Control in March 2026 under the Apache 2.0 license, a control plane for defining and enforcing agent behavior policies across heterogeneous deployments. Cisco AI Defense was already listed as a Day One integration partner for Agent Control before this acquisition was announced. That prior relationship made this deal less of a discovery and more of a formalization.

The stack Cisco is assembling piece by piece: agent identity and access through Duo IAM, vulnerability scanning through DefenseClaw, behavioral policy enforcement through Agent Control, and now evaluation and observability through Galileo into Splunk. Each announcement has been framed independently. This is the first one that makes the platform ambition explicit rather than implied. Whether Cisco ships it as an integrated system or a portfolio of adjacent products is what Cisco Live in June will start to answer.

Galileo had also positioned Agent Control as vendor-neutral community infrastructure, with Strands Agents, CrewAI, and Glean as early integration partners. That community positioning is now in Cisco's hands. Apache 2.0 licensing protects the open-source artifact but says nothing about where the roadmap goes. The governance question for Agent Control deserves a direct answer at Cisco Live.