Intutic vs Fiddler AI

Last reviewed: 2026-10-10

Intutic decides each agent tool call before it runs, across 43 harnesses: native hook gates and in-process SDK gates allow or block the call, and either can hold it for human approval. Around the gates, a policy proxy redacts sensitive data in model traffic, an MCP governance proxy governs MCP tools, an egress firewall and sandboxed execution stop the agent routing around governance, and a signed audit trail records every decision. Fiddler AI is an AI observability and guardrails platform: monitoring for predictive models, tracing and evaluation for LLM applications and agents, and Fiddler Centor Models, its own small models that score safety, faithfulness and PII. Its Control Plane for Coding Agents, announced in June 2026, attaches to an AI gateway you already run (LiteLLM, AgentGateway or Kong): it redacts or blocks PII and secrets in model requests and responses, and joins gateway traffic with agent telemetry for cost and adoption reporting. Enforcement inside the IDE, CLI and MCP boundary, which came with its April 2026 acquisition of Lumeus, is offered to design partners ahead of general availability; the Control Plane page now also claims blocking of risky tool use at that boundary, but its documentation does not yet describe it. Its Claude Code plugin, in public preview since October 2026, records sessions including tool inputs and outputs, and fails open. Fiddler judges the content of the model call; Intutic governs the actions the agent takes.

Comparison

	Intutic	Fiddler AI
Primary job	Runtime enforcement and audit for AI agents	Observability, evaluation and guardrails for ML models, LLM applications and agents
Where it enforces	Native pre-execution hook gates in 18 of the 43 harnesses and in-process SDK gates, plus request and response proxy, MCP governance proxy, egress firewall and sandbox	Inline at a third-party AI gateway (LiteLLM, AgentGateway 1.4.0 or later, or Kong), on model requests and responses; its Guardrails API from your own code
Coding agents	43 supported harnesses, including Claude Code, Codex, Cursor, GitHub Copilot, Windsurf, Cline and Gemini CLI	Any agent whose model traffic passes a supported gateway; telemetry setup documented for Claude Code and GitHub Copilot. The Claude Code plugin (public preview) traces tool inputs and outputs and fails open; Claude Cowork log ingestion is in private preview
Gemini CLI	A `BeforeTool` hook in `~/.gemini/settings.json` runs Intutic's gate before every tool call, MCP tools included, and blocks the call before it runs. `intutic gates install` installs it when `intutic init` finds Gemini CLI: `gemini` on the `PATH`, a project `.gemini/` directory or `~/.gemini/settings.json`. Antigravity CLI, which replaced Gemini CLI for individual users in June 2026, gets its own `PreToolUse` gate in `~/.gemini/config/hooks.json`	Named as an agent telemetry source at launch, with no setup guide in its docs. Gateway guardrails apply to Gemini CLI traffic routed through a supported gateway
Decisions	Allow, warn, require approval (the call is refused and queued for approval in Slack or the CLI), block, redact, re-ask, shadow	Allow, block, redact (Kong blocks only). Human approval for high-risk decisions is announced, not documented
Tool calls	The gate sees the tool name and its full arguments, such as the shell command, file path or MCP call, before the tool runs	PII or secrets in tool-call arguments block the request. Through LiteLLM, tool calls replayed in Anthropic-format history, as Claude Code sends them, are not scanned
Sensitive data	Pattern DLP for 17 kinds of secret and credential, and validated PII detectors for payment cards, IBANs and US Social Security numbers (email and phone opt-in), redacted or blocked; add your own patterns	Model-based PII detection (12 entity types by default, 27 in full mode, plus health entities) and about 42 credential formats
Content judgement	Pattern-based prompt-injection detection that steers or re-asks the model; tool-description poisoning detection. Self-hosted open-weight judges review responses against your rules after the fact and do not block	Centor Models score safety across 11 categories, including jailbreaks, and faithfulness to context; escalation of safety checks to a larger hosted model is in private preview. Its gateway integrations run only the PII and secrets checks
MCP	MCP governance proxy: server and tool allowlists, SSO-group clearance, DLP, approval holds, pinned tool definitions	Traces MCP tool calls through AgentGateway or LiteLLM; enforcement at the MCP boundary is in early access. The Fiddler MCP Server lets an agent query Fiddler data; it is not a gateway
Containment	Opt-in default-deny egress; sandboxed runs in a container or Firecracker microVM	None documented
Cost	Spend caps enforced before a request leaves; cost and token reporting by model, virtual key, developer, team (from SCIM groups), branch, commit and GitHub pull request	Cost, tokens and adoption by developer, team and model; cost per pull request and commit
Model monitoring	None	Drift, data integrity, performance and fairness for predictive models
Source	Open core (MIT)	Closed; its Claude Code plugin is Apache-2.0

Where Fiddler is stronger

Content judgement. Trained models score safety, jailbreaks and faithfulness, and its secrets check knows more credential formats. Intutic's prompt-injection detection is pattern-based and steers rather than blocks, and its self-hosted judges review after the fact instead of gating the call.
PII and PHI. Model-based detection of names, addresses, phone numbers, card numbers, IBANs and health identifiers. Intutic's built-in detectors are validated patterns: payment cards (Luhn check), IBANs (mod 97 check) and US Social Security numbers (issued ranges), with email and phone opt-in. Names, addresses and health identifiers need a model, which Intutic does not have yet.
Observability and evaluation. Trace exploration, more than 80 evaluators and custom judges for LLM applications and agents.
Model monitoring. Drift, data integrity, performance and fairness monitoring for traditional ML models.
Certifications. Fiddler states SOC 2 Type II and HIPAA compliance for its own service.

When to choose Intutic

Your agents run shell commands, write files and call MCP tools, and you need each call decided before it runs.
You use coding agents beyond Claude Code, or agent frameworks such as LangGraph and CrewAI.
You want risky calls held for human approval rather than only allowed or blocked.
You need egress control and sandboxing.
You want enforcement that does not depend on routing every agent through one gateway.

When to choose Fiddler AI

You need model-based PII, PHI, safety and faithfulness checks on model inputs and outputs across LLM applications.
You monitor traditional ML models for drift and fairness alongside LLM applications.
Your coding agents already route through LiteLLM, AgentGateway or Kong, and redacting PII and secrets there plus cost and adoption reporting is the main goal.

Use them together

Fiddler's guardrails sit on the gateway your model traffic already uses. Intutic's hook gates run inside the agent and do not depend on which gateway carries that traffic. Running both adds model-based PII detection on the model call to a pre-execution check on every tool call, and Fiddler can trace the same Claude Code sessions through its own telemetry integration.