Put people exactly where judgement matters
Full autonomy is rarely the goal. We design review lanes, approvals and escalation so people handle the exceptions and agents handle the volume.
How we approach it
A good human-in-the-loop design is specific about when a person is involved: below a confidence threshold, above a monetary limit, on a first-time customer, or when the agent is asked to do something irreversible.
We build the review interfaces too, with the agent's reasoning, the sources it used and a one-click approve, edit or reject, so review is fast and the feedback improves the system.
The review rate is a design target
A healthy system sends fewer items to review over time. We track it and tune thresholds as trust is earned.
- Audit trail of who approved what and when.
- Graceful fallback when no reviewer is available.
- Metrics on review rate and override rate.
Step by step
Define triggers
Rules for when a person must review.
Review lanes
Queues with context and sources.
Decide
Approve, edit or reject.
Learn
Decisions feed evaluation and tuning.
What you receive
Review interface
Everything needed to decide in one view.
Escalation rules
Confidence, value and risk thresholds.
SLA tracking
Queues that do not silently age.
Feedback loop
Reviewer decisions become training signal.
Common questions
Does human review defeat the purpose of automation?
No. Reviewing a drafted, sourced action is much faster than doing the work, and it lets you automate safely.
Who sees the review queue?
The roles you define, with permissions.
Can reviewers train the agent?
Their decisions are recorded and used to improve prompts, rules and evaluation sets.
Scope your agent system with an engineer
Bring the process you want to automate. We will talk through the architecture, the risks and a realistic first release.