Remediation · Pillar 03
The agent stays productive. Inside the leash.
Instead of flagging issues for a human to review, Hootleash provides real-time task routing and self-correction. When a system hits a boundary, Hootleash triggers a graceful degradation or safe shutdown - and lets the agent keep doing the work you hired it for.
Why HOOTL > HITL
Humans are speed bumps. Hootleash is a shock absorber.
A human-in-the-loop check on a high-frequency autonomous workflow is a bug, not a feature. It either blocks throughput or gets rubber-stamped. Hootleash replaces the speed bump with a tiered remediation playbook the system runs on itself.
- Throttle → reroute → degrade → halt, in that order
- Each tier is testable, observable, and reversible
- Closed-loop learning: post-incident, the leash tightens automatically
- Humans get a digest. Pagers stay quiet.
Designed for continuous auto-remediation · escalations only when policy demands them · post-mortems, not pages.
Capabilities
A response for every level of risk.
Hootleash ships pre-built playbooks for common failure modes - and an authoring surface for the ones unique to your business.
Throttle
Reduce request rate, batch size, or model temperature when leading indicators drift.
Reroute
Send traffic to a peer agent, a smaller model, or a deterministic fallback path.
Degrade gracefully
Drop to read-only, simulation, or last-known-good mode without interrupting user-facing flows.
Halt safely
Issue a signed kill-switch with rollback, state snapshot, and downstream notification.
Sub-second response
Detect → decide → enact in the same trip the agent was already making. Faster than your alerting pipeline could even page someone.
Provenance & rollback
Every remediation captures inputs, decision, action, and a signed reversal plan.
Time-aware playbooks
Quiet hours, board-meeting freezes, end-of-quarter conservative modes - first-class scheduling.
Human-on-the-loop notifications
When humans should know, they get a single, scoped, batched notification - never a flood.
Anatomy of a remediation
What a single boundary breach is designed to look like.
Step 1
Detect
Pre-action consequence model scores the next agent step against the dollar threshold for the workflow.
Step 2
Decide
Policy engine matches the breach to the right playbook tier - throttle, reroute, degrade, or halt.
Step 3
Enact
Traffic moves to a peer agent or a deterministic fallback. The original agent is downgraded and re-prompted.
Step 4
Seal
Event written to a tamper-evident log and cross-walked to EU AI Act, NIST AI RMF, and your internal MRM controls.
Ready when you are
Replace pagers with playbooks.
We're building Hootleash in private design partnership with a small number of regulated enterprises. If you run autonomous AI in production, get in touch.
Pre-launch · design partner program open · early access 2026