A few weeks ago I wrote about the comprehensive agentic AI security stack that Cisco shipped at RSA 2026, researching what happens before agents go live, while they’re working, and when something goes wrong. Cisco’s planned acquisition of Galileo — announced last week — extends that middle phase faster than I expected. Cisco already had the security half of the “while agents are working” problem covered. What Galileo adds is the quality half, and together those two halves give Cisco one of the more complete agent operations stories in the market right now.
Cisco announced the deal on April 9, with close expected before the company’s fiscal year ends in July; the timing lines up with a moment in the marketplace when getting agents into production is the challenge everyone is focused on. Knowing whether those agents are doing their job correctly, at scale, in real time is the part almost nobody has figured out yet.
Cisco’s press release makes it clear this is Splunk’s deal. Galileo slots directly into the Splunk Observability portfolio, and the choice of landing spot matters more than it looks. If Cisco wanted to brand this as a security story, then its AI Defense or Hypershield portfolios would have provided a natural home. Both are part of the Security Business Group, both are where Cisco has been pouring agent-related investment since last year, and both would have given the deal a “Secure your agents” headline to line up neatly with the RSA announcements from a few weeks ago.
Galileo is going into the observability side of the house instead, landing inside the portfolio Cisco built around its $28 billion Splunk acquisition of March 2024. The message is that agent quality belongs in the same operational layer as application monitoring, infrastructure telemetry, and log analysis. Drift, evaluation, and “Is this answer any good?” are run-the-business concerns that sit next to the performance and availability questions that operations teams have been answering for years. That framing has real implications for which teams inside the customer own agent operations, how it gets budgeted, and which vendors Cisco is actually competing with in this space.
Splunk Observability Cloud already has a product in this space called AI Agent Monitoring that went GA in Q1 2026 and tracks latency, errors, token usage, hallucinations, bias, and drift for AI agents and applications. Cisco AI Defense is already integrated into it to detect security and compliance signals for things like prompt injection and PII leakage. Galileo extends this existing foundation rather than building one from scratch, which could make a difference in how fast the integration can actually ship.
I’ve been watching Galileo for a while, and the reason it matters here is that it built its platform specifically to address the production AI problem rather than grafting model monitoring onto an existing APM stack. Galileo has been focused on LLM reliability since before most of the enterprise market treated it as a real problem. That gives it a longer operating history on “Is this output actually correct?” than most of the vendors currently pitching agent observability.
The platform reflects that focus and is organized around three connected capabilities Galileo calls evaluation, observability, and protection. Evaluation handles pre-production work, where teams build golden test sets and score models or prompts against pre-built or custom metrics to decide whether something is good enough to ship. Observability handles production monitoring once that workload is live, tracking quality, cost, and behavior over time. Protection sits in the request path and enforces policy before a bad output reaches a user. All three are designed to work together, so production signals feed back into evaluation instead of living in disconnected dashboards.
The piece I find most interesting technically is Luna, Galileo’s line of fine-tuned small language models built specifically for evaluation work. Most LLM-as-judge approaches call a frontier model on every production request, which makes running evaluation at meaningful traffic volume prohibitively expensive. Luna inverts that equation by training specialized SLMs with metric-specific heads. (You can dig into the details of this in a research paper recently published by the Luna team.) When evaluation cost drops far enough that you can run it continuously instead of on a sample, you stop treating it as a periodic audit and start treating it as always-on quality control. That shift changes how engineering teams actually use agents in production, and it looks a lot more like the kind of continuous monitoring that ops teams already do.
Splunk Observability Cloud is built on the logs, metrics, traces, and dashboards that a generation of operations and platform engineers relied on to run modern applications. That architecture is excellent at answering whether an application is up and whether it’s running fast, and it scales to the traffic volumes that modern enterprises throw at it. What the architecture was never designed for is judging whether the output of a language model is any good. Splunk retrofitted it with AI Agent Monitoring to close part of that gap, but retrofitting only gets you so far.
Judging output quality turns out to be the hardest part of running agents in production. The failure modes look nothing like a 500 error or a slow database query. An agent can return a plausible, grammatical, confidently wrong answer while every latency and uptime metric on your dashboard stays green. To combat this, you need evaluation tooling that runs alongside the telemetry, scoring outputs against reference answers, catching hallucination signatures, and flagging behavioral drift as the underlying model updates underneath you. This is especially important because, when you stack agents on top of models, every bad output becomes a multi-step forensic question about which reasoning step introduced the error and whether the failure is even reproducible. Traditional tracing was never built to capture semantic content at that depth.
Dropping Galileo into Splunk Observability Cloud gives Cisco a story the rest of the observability market can’t credibly tell without a similar acquisition or a multi-year product rebuild. Most of those platforms are shipping LLM observability features, to be fair, and they’re iterating quickly. But the depth of evaluation tooling and the agent-lifecycle focus is where Galileo simply has a head start, and where Splunk on its own had the most ground to cover.
Cisco has been assembling its agentic AI story one component at a time since last year. AI Defense secures the models and the inputs that hit them. Hypershield handles east-west security in the GPU clusters where the workloads actually run. AgenticOps applies agents to the network itself, automating telemetry collection and root-cause analysis across Cisco’s own footprint. Silicon One provides the networking fabric underneath all of it, and the NVIDIA OpenShell partnership extends that fabric into multi-agent enterprise deployments at the edge.
What Cisco didn’t have was a layer that sits with the developer building an agent and follows that agent from prototype all the way through production. Galileo provides exactly that layer. With it, Cisco has a near-complete story across securing an agent, running the infrastructure the agent depends on, monitoring the agent’s behavior in production, and enforcing runtime guardrails on what the agent does next. Most hyperscalers don’t have all of those pieces under one logo yet. Neither do most pure-play security vendors. Neither do most pure-play observability vendors.
Look at the pairing with AI Defense specifically. AI Defense is already integrated into AI Agent Monitoring, so the infrastructure for pairing security and quality signals is in place today. AI Defense handles the model-boundary work, catching prompt injection, jailbreaks, PII leakage, and policy violations. Galileo deepens the quality side by catching semantically wrong outputs, hallucinations, and drift that don’t show up as security events. These are adjacent detection problems, and most enterprises aren’t anywhere near understanding that yet, let alone trying to solve it. The very few leading organizations far enough down the agentic AI path are stuck stitching multiple unrelated tools together, usually with a platform team that would rather be shipping features. Putting both capabilities under one roof — and ideally one control plane — is the kind of consolidation play that CISOs respond to, especially those who have been asked to underwrite an agentic AI rollout without a clear view of the risk surface.
I’ve been building the case for why this deal makes sense, and I believe that it does make sense in terms of Cisco’s product portfolio and market strategy. Where I’m less certain is how smoothly the integration plays out in practice.
These integrations are genuinely hard. I can guess what you’re thinking: Thanks for stating the obvious, Mike. But it matters more than usual here because the agent observability space is moving fast enough that even short integration delays can translate into real competitive exposure. Galileo is a significantly smaller company than Splunk, which should in theory make for a cleaner integration. Yet there’s a potential downside: I’m not singling out Cisco here, but we’ve seen the industry pattern enough times to know that big companies have a tendency to absorb smaller ones and lose what made them good in the first place.
Then there’s the developer experience. Galileo’s primary audience is the developer inside the enterprise, which is a different buyer than Splunk typically sells to. Developers want fast self-serve tools they can try without a procurement conversation. The Splunk/Cisco enterprise sales motion could easily crush that. Cisco needs to keep a developer-first path alive and resist the urge to bundle Galileo into something only a procurement team can love.
There’s also a near-term question about competitive response. Several security vendors have agentic AI ambitions, albeit without Galileo-level observability depth, and I’d expect at least one acquisition attempt in the same space inside the next two quarters. Established observability vendors will most likely respond by accelerating their own agentic AI roadmaps rather than buying. Either way, the window is likely quite narrow.
Assuming this deal goes through on schedule, I’ll be watching closely for three things.
The bigger question this raises is whether agent observability remains a category of its own or folds back into general-purpose observability. My read is that it will stay separate for at least a couple more years. The evaluation and guardrails work is genuinely different from logs, metrics, and traces, and the buyers are often different from the traditional APM teams. If that holds, Cisco just bought the best seat in a category that is addressing the barriers standing between enterprises and actually trusting their agents in production.
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