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Rigorix vs Backloop

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Rigorix compared with Backloop
DetailRigorix logo RigorixBackloop logo Backloop
What it doesExecution governance for consequential AI-agent operations Catch where AI agents get stuck on your API or MCP server, rank problems by impact, and draft fixes as pull requests
StageNot given Not given
FoundedNot given Not given
Based inNot given US
Pricing modelFreemium Freemium
PricingFree engine (MIT/Apache-2.0 open source). Team tier: €1,900/month including 25 writing seats and 50,000 governed actions per month; overage €0.02 per action above 50k monthly. Enterprise: from €90,000/year. Design partner program: €18,000 for 90 days (credited against first annual contract). Add-ons Open source: free. Team: $199/month with 14-day free trial (1 API or MCP service, 25,000 reports/month, 15 coding-agent runs/month). Business: $799/month (up to 3 services, 250,000 reports/month, 60 coding-agent runs/month). Extra coding-agent runs: $6 each.
Key features
  • Authority revalidation at dispatch time against live conditions
  • Approval binding to canonical intent with single-use, time-bound enforcement
  • Sequence and prerequisite policy enforcement at plan time and runtime
  • Effect identity matching to prevent duplicate actions across runs
  • HMAC-signed, hash-chained, and Ed25519-anchored evidence
  • Cross-session capacity ledger for aggregate exposure control
  • Verifiers in five languages for independent offline verification
  • MCP server support for integration with Claude Code, Cursor, and other agents
  • Air-gapped deployment with documented key ceremony on Enterprise
  • Deterministic Rust enforcement engine with no models in the decision path
  • Session replay for MCP servers to identify failed agent sessions
  • Clustering reports by agent goals and workflows
  • Priority ranking weighted by frequency, failed workflows, and affected accounts
  • Claude Code generates pull requests with tests
  • Review queue with diff, tests, agent reports, and evidence
  • Isolated git worktree prevents unauthorized code push
  • Schema validation and rate limiting for feedback
  • Open Agent Feedback Protocol with TypeScript and Python SDKs
  • GitHub integration for draft PR creation
What makes it different
  • Authority is revalidated at the moment of execution, not just at approval time
  • Signed, verifiable evidence allows third-party audit without trusting the vendor
  • Deterministic enforcement with no AI models influencing the gate decision
  • Pricing on governed actions, not seats, aligned with consequence not headcount
  • Refusals and verification are never metered or charged
  • Only AI agent failures on successful HTTP responses get detected and ranked
  • Automated pull request drafts with your approval required before shipping
  • Session replay captures failures even when agents don't report them
  • Pay only for coding-agent runs, not feedback submission
  • Open protocol and SDKs can be self-hosted at no cost
Community votes00
DR (Domain Rating by Ahrefs)2 +1 (up) since last check1 no change since last check
Trust Flow (Majestic)01
Citation Flow (Majestic)014
Referring domains (Majestic)04

5 details filled in for both companies.

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