failproof ai vs galileo

What Each Product Is

Galileo is built around evaluation: 20+ built-in evals for RAG, agents, safety and security, custom evaluators, agentic metrics such as Tool Selection Quality and Session Success, and Luna-2, small 3B and 8B evaluation models that run on the Enterprise tier. CLHF tunes a judge metric from as few as five annotated records.

Two things changed in 2026. Cisco completed its acquisition of Galileo in May, and on 7 August Galileo's release notes announced that "Galileo is now Splunk Agent Observability"; galileo.ai still publishes the Free, Pro and Enterprise plans. And Galileo Protect, its runtime guardrail product, was deprecated in June in favour of Agent Control, an Apache-2.0 control plane that checks LLM and tool inputs and outputs and returns deny, steer, warn, log or allow. You run the open-source server yourself; a centrally managed version is for Enterprise customers.

Failproof AI finds agent failures and then prevents them. It connects natively to twelve agent harnesses, including Claude Code, Codex, Cursor, GitHub Copilot CLI and Goose, and its Python SDK traces custom agents. Evaluations run in the cloud on every trace and session: code checks, LLM judges, and the eval set you already have, brought in as it is. Audits group failures into findings with a severity and a recommended fix; from an issue, generate policy drafts a policy you backtest against calls your agents already made, and the open-source failproofai CLI enforces it at PreToolUse, before the tool runs.

Both evaluate, and both can deny a tool call. The differences are where the check lives, where it comes from, and which tier it sits on. Galileo's controls attach to code you own, and its hosted guardrails are Enterprise. Failproof AI's policies attach to the harness, come out of the failures its evaluations found, and run from the free tier.

Features Compared

Galileo's column comes from its public site, pricing page and docs as of September 2026. Every page used is linked under Sources.

Both find failures. The rows that separate them come after detection: whether a finding becomes a fix you can test against your own past agent activity, and whether the next failure is stopped where the agent acts, on the plan you already have.

Where Galileo Stands Out

• A metric library. 20+ built-in evals for RAG, agents, safety and security, including agentic metrics such as Tool Selection Quality and Session Success.
• Its own judge models. Luna-2, 3B and 8B evaluation models listed at $0.02 per million tokens, available on Enterprise.
• Judges tuned from a few labels. CLHF regenerates a judge's prompt from expert feedback on as few as five records.
• Enterprise track record. Twilio, Comcast, HP and ServiceTitan were named at its 2024 Series B, and it now sits inside Cisco through Splunk.
• Controls for agents you build. Agent Control integrates with LangChain, CrewAI, Google ADK and AWS Strands, with pluggable evaluators such as Luna, NVIDIA NeMo and Cisco AI Defense.

Where Failproof AI Goes Further

• Prevention that comes from what you found. Audits group failures into findings with a severity, evidence sessions and a recommended fix; generate policy drafts a policy from the issue, and a backtest replays it against calls your agents already made before you deploy it in observe mode and enforce it. In Galileo, turning a finding into a control is code you write.
• Bring your own evals. Failproof AI runs the eval set you already have (DeepEval, Ragas, promptfoo or your own) in the cloud as it is, alongside code checks and LLM judges, so moving in does not mean re-scoring everything against a new metric library.
• The hook, not your code. Agent Control makes a function governable with a @control() decorator, which assumes the function is yours to change. A coding agent's Bash and Write tools run inside the harness, and the harness exposes hooks. That is where Failproof AI runs, in twelve harnesses.
• A pack on day one. failproofai policies add FailproofAI/policies adds the coding agent pack: 38 policies, 10 on by default, including block-push-master, block-env-files, block-sudo and the sanitize-* redactors. Agent Control ships evaluators; the rules are yours to write.
• Enforcement on the free tier, with no server. The MIT CLI enforces locally with no control plane to run. Galileo lists real-time guardrails under Enterprise, and open-source Agent Control needs its own server, which the quick start runs with Docker Compose and PostgreSQL.
• Published prices all the way up. Overage rates and SSO at $599 a month are on the pricing page. Galileo's Pro and Enterprise plans start with a demo.

A policy for a failure your evaluations keep flagging takes a few lines of JavaScript:

// prod-deploy-policies.js: no production deploys from a coding agent
import { customPolicies, allow, deny } from "failproofai";

customPolicies.add({
  name: "block-prod-deploy",
  description: "Deny production deploy commands from coding agents",
  match: { events: ["PreToolUse"] },
  fn: async ({ toolName, toolInput }) => {
    if (toolName !== "Bash") return allow();
    const command = String(toolInput?.command ?? "");
    if (/\b(deploy|release)\b/i.test(command) && /\bprod(uction)?\b/i.test(command)) {
      return deny("Production deploys go through CI after review, not through the agent.");
    }
    return allow();
  },
});

Install it locally with failproofai policies --install --custom ./prod-deploy-policies.js, or publish it from the policy editor, backtest it, and deploy it in observe mode before you enforce it. The deny lands before the command runs, and the reason goes back to the agent, so it can take the CI route instead of retrying.

One Failure, Both Tools

A coding agent in Claude Code, asked to "ship the hotfix", runs the production deploy script itself from an unreviewed branch. Nothing errors, and the transcript reads like a job well done.

1. Galileo: evaluate, then write a control

   Galileo's evals score the traces you send, and a custom metric can flag the deploy after the fact. To stop the next one you would write an Agent Control control and attach it with a decorator, but the deploy runs through the harness's built-in Bash tool, not a function in your code. Check Galileo's current docs for harness support before counting on it.

2. Failproof AI: evaluate, audit, fix, block

   An evaluation flags the session, and an audit groups every unreviewed production deploy into one finding with a recommended fix. From the issue, generate policy drafts the PreToolUse rule above; the backtest shows which past commands it would have denied, and you enforce it.

3. The next attempt

   With Failproof AI, the deploy is denied before it runs, and the reason goes back to the agent so it can open a PR and take the CI route. With Galileo, the deploy runs unless a control you built and host sits in its path.

Both products can see the failure. Failproof AI turns it into a rule that stops the next attempt, from the free tier, with nothing to host.

Pricing

At the entry tier the two are almost the same price, and both let you start free without a sales call. They part ways one step up: Galileo's pricing page routes Pro, as well as Enterprise, to "Book a Demo", so its first paid tier starts with a conversation.

Runs and traces are different units, so read the volume rows as rough. Failproof AI publishes the path upward: overage, SSO at a listed price, and blocking in the free tier. Galileo Enterprise, where Luna-2 and hosted guardrails live, is priced by quote.

The Cisco Question

Galileo's ownership changed this year: Cisco announced the deal on 9 April 2026, Splunk said it was complete on 28 May, and the product has carried the Splunk Agent Observability name since 7 August. Galileo is still sold and documented, and Agent Control is Apache-2.0 whoever owns it. If renewal terms or the docs location matter to you, ask your account team; neither company has published those details.

Moving from Galileo, or Running Both

If Galileo already scores your agents, you do not have to remove it. Add Failproof AI where your agents act: its evaluations and audits find failures across every trace and session, including with the eval set you already have, and its policies block the failing action inside the harness before it runs, from the free tier. When you want one tool for both halves, Failproof AI covers the detection and the prevention.