Supervision infrastructure for AI agents

Know which agent work actually needs a human.

RightBounds turns agent traces, code changes, tests and outcomes into risk-based supervision—so humans review what matters and agents earn more autonomy over time.

Request a pilot See a sample run

Starting with coding agents and AI-native engineering teams.

coding-agent-17

Add seat-proration support

Deep review

ScopeEXCEEDED

AuthorityMODIFY CODE

DataTEST ONLY

ReversibilityHIGH

VerificationINCOMPLETE

ReviewDEEP

Different actions deserve different bounds.

TraceAssessSuperviseAdjust bounds

The attention gap

Agent output is scaling faster than human attention.

Treat every agent-generated change the same and one of two things happens: senior reviewers spend time on work that does not need them, or consequential changes receive less scrutiny than they deserve.

Review everything

Agents move quickly, but senior review becomes the bottleneck.

Trust everything

Subtle requirement omissions, side effects and weak verification can escape.

Ask the agent if it is safe

The executing agent should not be the only judge of whether its own work deserves independent scrutiny.

How it works

From trace to supervision.

Trace data is evidence. The useful layer decides what to inspect, what remains uncertain and how much authority the next action deserves.

01
Capture evidence
Intent, agent activity, changed files, commands, tools, tests and review history.
Initial supervision direction
02
Assess consequences
Understand novelty, scope, sensitive systems, reversibility and missing verification.
Initial supervision direction
03
Allocate human attention
Recommend deterministic checks, lightweight review, deep review or random audit.
Initial supervision direction
04
Record the decision
Preserve what was checked, what remained uncertain and why the work was approved.
Initial supervision direction
05
Learn from outcomes
Link corrections, reversions and incidents back to the work and adjust future bounds.
Direction: learning loop

Multidimensional authority

Autonomy isn't one dial.

An agent can be trusted to modify application code while remaining unable to deploy it. It can operate freely on test data while requiring approval for customer data. Low-risk changes can move quickly while authentication or billing changes receive deep review.

Action	Scope	Authority	Data	Verification	Review	Execution
Documentation	Repository	Modify	Public	Deterministic	Light	PR only
UI change	Frontend	Modify	Test	Visual + tests	Sample	PR only
Core logic	Application	Modify	Test	Full suite	Deep	PR only
Authentication	Identity	Propose	Sensitive	Security suite	Deep	Human merge
Billing	Payments	Propose	Financial	Idempotency	Deep	Human merge
DB migration	Schema	Draft only	Customer	Rollback proof	Deep	Blocked
Production deploy	Production	None	Live	Release gates	Authorise	Blocked

Supervision packet

See what a reviewer actually needs.

Not a confidence score. A bounded account of intent, observed work, evidence, uncertainty and the specific questions worth a human's time.

Explore the full synthetic sample

Illustrative synthetic exampleDeep review

Task

Add prorated billing when seats change mid-cycle.

Why

Billing changed across two execution paths. A migration moved outside task scope. Unit tests passed; idempotency and concurrent-payment behaviour were not exercised.

7 files changed18 tests passed2 checks missing1 scope exceptionProduction: none

Available as a founding pilot

Start with a Review Capacity Audit.

Before installing another agent platform, understand how your team is already spending human attention.

We analyse approximately 30–100 historical agent-assisted changes or workflow runs, subject to what your team can safely provide.

Founding pilots from£750

Request a pilot

AUDIT DELIVERABLES

Baseline of current review allocation

Examples of likely over-review and under-review

Repository-specific risk categories

Proposed evidence requirements

Recommended review-depth policy

Random-audit strategy

Candidate actions for greater or lower autonomy

Concise findings report

Historical evidence first.The first engagement requires no production write access.Private-repository arrangements and data handling are agreed before any private artefacts are provided. Do not upload source code through this website.

Early customer profile

Built for teams already feeling the review bottleneck.

AI-native engineering teams

Multiple coding agents producing meaningful development work every week.

Technical founders

Using Claude Code, Codex, Cursor or similar tools while still personally reviewing consequential changes.

AI development agencies

Delivering agent-assisted software where review quality and client confidence both matter.

Independent supervision

The author shouldn't be the only auditor.

General coding agents can inspect diffs, run tests and critique work. RightBounds is aimed at the layer they cannot independently provide: organisation-level evidence, review allocation, historical outcomes and formal limits on their own autonomy.

Independent evidence

Do not rely exclusively on the executing agent's own confidence.

Cross-run history

Learn from previous corrections and failures, not one isolated prompt.

Human calibration

Measure where expert attention changes outcomes.

Outcome feedback

Connect later reversions and defects to earlier supervision decisions.

These are product principles and direction—not a claim that a proprietary outcome dataset already exists.

The longer arc

Give every agent the right bounds.

Coding review is the starting point. The same supervision model can eventually govern other consequential agent actions: deployments, customer communication, procurement, data changes and financial operations.

TRACE↓ASSESS↓SUPERVISE↓ACT WITHIN BOUNDS↓OBSERVE OUTCOME↺

Review capacity audit

Find out where your review time is actually going.

If your team is already producing enough agent-assisted work that reviewing everything feels unrealistic, we want to study the workflow.

Request a pilot hello@rightbounds.com