LLM guardrails platforms that can block/redact outputs and trigger webhooks before a tool executes

Most teams don’t discover a dangerous agent action until after it runs—after a tool call hits production data, an off-brand answer ships to a customer, or a prompt injection hijacks the flow. At that point, observability alone is too late. You need LLM guardrails that sit in the execution path, can block or redact outputs in real time, and can trigger webhooks before a tool executes.

Quick Answer: Galileo is an AI reliability and guardrails platform that evaluates every LLM/agent interaction in real time, then blocks, redacts, overrides, or routes via webhook before tools execute—so agents can’t quietly hallucinate, leak PII, or follow injected instructions into production.

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The Quick Overview

• What It Is: An LLM guardrails and runtime protection platform that evaluates inputs, outputs, and tool calls, then enforces policy with actions like block, redact, override, or webhook—under a tight latency budget.
• Who It Is For: Teams shipping RAG systems and AI agents into production who can’t afford blind spots—enterprise engineering, ML, and security teams that need deterministic control, not “monitoring after the fact.”
• Core Problem Solved: LLMs and agents fail in expensive, hard-to-predict ways (hallucinations, prompt injection, PII leaks, wrong tool actions). Galileo turns evaluations into production guardrails so those failures are intercepted before users see them or tools execute.

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How It Works

At a high level, Galileo connects your agent or RAG app to an evaluation and guardrail layer that sits in the request path. Every interaction—user input, model output, tool call—is scored against safety, security, hallucination, and quality evaluators. Those scores power guardrail policies that decide what happens next: pass through, redact, override with a safer response, or trigger a webhook to your own workflow before anything executes downstream.

Under the hood, three phases matter:

1. Evaluate (Design & Calibration):

   • Use Galileo’s Evaluation Engine to test prompts, models, and agent flows with 20+ out-of-the-box evaluators for hallucinations, RAG quality, safety, and security.
   • Capture ground truth from synthetic test sets, dev data, and early production traffic.
   • Incorporate subject-matter-expert annotations and optional LLM-as-judge evaluators to define what “good” and “unsafe” look like in your domain.
   • Distill these evaluators into compact Luna / Luna-2 models that can run continuously at low latency and cost.

2. Signals (Detection in Production):

   • Once live, Galileo ingests 100% of traces (sessions → traces → spans) from your agent or RAG system.
   • Signals automatically analyzes these traces to surface patterns—hallucinations that slip past your test set, new prompt injection variants, PII exposures, policy drift, or cascading tool-call failures.
   • From any discovered signal, you can generate a new evaluator or refine an existing one—so unknown unknowns become codified guardrails instead of recurring fire drills.

3. Protect (Real-Time Guardrails & Actions):

   • Protect runs your selected evaluators (backed by Luna-2 small language models) on every input and output, with multi-headed models evaluating 10–20 guardrail metrics at once.
   • Scores feed guardrail policies created in a central UI or via API.
   • On breach, Protect intercepts the content and executes actions: block the response, redact sensitive spans, override with a safer answer, or trigger a webhook before a tool executes.
   • All interventions are logged with full audit trails, and policies can be versioned and rolled back without redeploying code.

This Evaluate → Signals → Protect loop is core: pre-production evals become production governance; scores don’t just sit in dashboards, they control agent behavior.

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Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Evaluation Engine	Runs 20+ out-of-box evaluators (RAG quality, hallucinations, safety, security, agent/tool behavior) plus custom evaluators; distills them into Luna / Luna-2 models.	Turns messy LLM behavior into measurable metrics you can trust, then makes those metrics cheap and fast enough to run on every request.
Protect (Real-Time Guardrails)	Intercepts inputs/outputs, scores them with Luna-2, and applies guardrail policies (block, redact, override, webhook) with sub-200 ms latency.	Stops hallucinations, PII leaks, prompt injection, and bad tool actions before users or systems are impacted.
Signals (Proactive Detection)	Continuously analyzes 100% of traces to surface new failure modes; lets you promote detected patterns into reusable evaluators and guardrails.	Finds “unknown unknown” failures early and converts them into automated protections, so reliability improves over time without chasing logs.

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Ideal Use Cases

• Best for agent workflows with tool access: Because Galileo can evaluate every step in a multi-tool, multi-hop trace and trigger webhooks before a tool executes. That means you can, for example, block a fund-transfer action if the explanation looks hallucinated, or escalate to a human if a security-sensitive tool is invoked with risky context.
• Best for RAG systems serving customers or employees: Because Protect can detect hallucinations, off-brand answers, and missing citations in real time—then override the response with a fallback answer, redact sensitive spans, or route the session for review without degrading latency or coverage.

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Limitations & Considerations

• Not a generic “chat with logs” solution: Galileo intentionally avoids the “search your logs” pattern as the primary defense. Instead, it treats evaluation and guardrails as a detection-and-prevention problem. You’ll still have rich traces and metrics, but the value is in automated interception, not manual spelunking.
• Requires upfront evaluator design: To get precise protections that match your domain, you’ll want to invest in evaluator design and SME annotation. Galileo accelerates this with LLM-as-judge generation and CLHF-style tuning, but teams should plan for an initial calibration phase rather than expecting perfect out-of-box behavior.

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Pricing & Plans

Galileo’s pricing is designed around traffic volume, evaluation depth, and deployment requirements (SaaS, VPC, or on-prem), not vague “seats.”

While exact numbers will depend on your scale and security posture, teams typically structure around:

• Growth / Team Plan: Best for product and ML teams moving their first RAG or agent workloads into production, needing:

  • 100% trace coverage at modest volume (e.g., up to a few thousand traces per month).
  • Core Evaluate + Protect, with a standard library of guardrail metrics.
  • SaaS deployment, SSO, and access to Luna-2 powered evaluators.

• Enterprise Plan: Best for platform teams standardizing evaluation and guardrails across many apps, needing:

  • High-throughput coverage (e.g., 10,000+ requests/min), multi-environment support, and custom SLAs.
  • Full Evaluate + Signals + Protect, with advanced security and safety evaluators, plus custom evaluator training.
  • VPC or on-prem deployment, SOC 2 Type II posture, HIPAA-eligible infrastructure with BAAs, dedicated inference servers, and granular RBAC/audit logging.

For specifics, volume tiers, and deployment options, it’s best to talk directly with Galileo’s team.

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Frequently Asked Questions

Can Galileo actually block or redact model outputs before they hit my user or tool?

Short Answer: Yes. Protect sits inline in your request path and can block, redact, or override outputs—and trigger webhooks—before they’re returned or executed.

Details:
Protect is built as an enterprise-grade LLM firewall. Every input and output is evaluated against guardrail metrics (e.g., hallucination risk, PII exposure, prompt injection patterns, policy violations). If a policy threshold is breached, Protect doesn’t just log the issue; it intercepts the content and executes your configured action:

• Block: Stop the response entirely and return a safe error or fallback message.
• Redact: Remove sensitive spans (like PII or secrets) while letting the rest of the output pass.
• Override: Replace hallucinated or off-brand answers with a safer, pre-approved template or a fresh call to a safer prompt/model.
• Webhook: Notify or hand off control to your own systems before a tool executes—e.g., send the span to a risk service, require human approval, or log into your SIEM.

All of this runs with sub-200 ms overhead using Luna-2 SLMs, so you can run guardrails continuously on 100% of traffic instead of sampling.

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How is Galileo different from using a big LLM-as-judge on every request?

Short Answer: Galileo distills evaluators into small, multi-headed models (Luna / Luna-2) so you get near judge-level quality at a fraction of the latency and cost—and can run them on every request.

Details:
Heavyweight LLM-as-judge evaluators are excellent for one-off experiments but rarely viable for production guardrails: they’re slow, expensive, and can’t cover 100% of traffic without blowing up your latency and cost budgets.

Galileo solves this in three steps:

1. Evaluator Design: Start with out-of-box evaluators and optional LLM-as-judge patterns to define what “good” looks like (hallucination thresholds, allowed content, safe tool usage).
2. Distillation to Luna / Luna-2: Galileo distills these evaluators into compact SLMs—not generic LLMs, but evaluation-specific models. Luna-2 can evaluate 10–20 guardrail metrics at once with sub-200 ms latency and roughly 97% lower cost than GPT-style judges.
3. Always-On Execution: Because evaluators are cheap and fast, you can run them on every input and output, across all sessions, traces, and spans. That enables real-time blocking, redaction, overrides, and webhooks—not just retrospective scoring.

The net effect: instead of choosing between “good evals” and “production viability,” you get both.

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Summary

If your LLM guardrails can’t block or redact outputs and trigger webhooks before a tool executes, you’re still flying blind—watching failures after they’ve already impacted users or systems. Galileo is built to fix that by turning evaluation into a first-class production primitive:

• Evaluate models, prompts, and agents with domain-specific metrics.
• Use Signals to discover new failure modes from real traffic.
• Enforce Protect guardrails inline, powered by Luna-2, with sub-200 ms actions that can block, redact, override, or trigger webhooks before any risky output or tool call goes live.

This isn’t just AI monitoring. It’s evaluation-as-governance—so your agents behave reliably, under real-world constraints of latency, cost, and compliance.

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