Edits left in tickets
Reviewers correct a reply, but the edit never becomes a test case or approved example.
Human-in-the-loop for AI agents
Make human review useful beyond the current conversation.
Decide which actions need approval, when the agent should hand off, and who can approve corrections. Save useful edits as examples and test whether they help the agent handle similar requests.
Don't lose the edits your reviewers already make.
A correction can help more than one customer, but only if it's recorded, approved, and used in the right context.
Manual review of every turn
Reviewing every reply may be necessary for safety, but it doesn't by itself change what the agent does next time.
Corrections used in the wrong context
An exception approved for one customer may be wrong for another. Record where each example applies.
A queue without review rules
Reviewers need clear approval rules, source records, and a way to test whether corrections help.
Save approved corrections and test their use
We help your reviewers record corrections and restrict where the agent can retrieve them. Then we test whether those examples reduce repeat mistakes.
Reviewer workflow design
Decide when reviewers should correct, hand off, or reject a case, and what to record when they approve it.
Store approved examples
Store context, failed output, rewrite, critique, version, and retrieval restrictions outside model weights.
Measure repeat mistakes
Test whether retrieved corrections reduce edits and escalations on the same failure cluster.
Agree who can approve what
For voice and support agents, a correction may affect real customer actions. Reviewers need clear authority and rules for approving examples.
Correction vs escalation policy
Decide which mistakes need corrected actions, which need a handoff, and which the agent must leave to a person.
Reviewer rubric and authority
Specify who can approve, what they may change, and how disagreements are resolved before an example enters retrieval.
Use corrections on similar tasks
Store approved examples in a searchable library with filters for the relevant task and policy.
Apply the same loop to call outcomes with voice AI testing servicesMeasure whether reviewed corrections improve outcomes via LLM evaluation services
Human review rules
Decide when a person must review an action and when an approved correction can be reused.
What human-in-the-loop means
A person reviews, corrects, or takes over agent work before or after an action. Approved corrections can also become examples for similar cases.
When to review
Require approval before high-risk actions. Review lower-risk work afterwards where appropriate. Hand off when policy requires it or the agent can't proceed safely.
Decision matrix
Automate low-risk repeats, review ambiguous cases, escalate mandatory or unsafe cases.
Trade-offs
More human review raises latency and cost; less review raises quality risk. Scope the loop to expensive failure clusters first.
Disagreements and records
Record who approved each example, ask a policy owner to resolve conflicts, and keep the source conversation.
Who this is for
- Teams whose humans already edit agent output in production
- Voice and support agents that fail the same intents repeatedly
- Compliance-sensitive workflows that need approval gates on reusable examples
- Teams that want to reduce repeated review work
Sprint shape for HITL agents
Review
Check review volume, recurring mistakes, and gaps in approval or source records.
Prepare examples
Save approved corrections with source records and rules for reuse on one task.
Evaluate
Compare human edits, handoffs, and completed tasks before and after the change. Agree limits for deployment.
Approval gates before reuse
A human edit is not automatically safe to retrieve. Scope filters, versioning, and retirement rules come before broad rollout.
Limitations
- Reviewing every reply doesn't by itself change the model's future behavior.
- A correction for one case may be wrong for another. Record where it applies.
- A review queue still needs approval rules and a way to measure results.
- A human edit is not automatically safe to retrieve.
How we test changes
We review real conversations with your team and agree what the agent should do differently. Approved corrections can become training data, test cases, or examples the agent looks up. We compare each change with your best existing setup on separate test cases, then check for new failures in the software you plan to deploy.
References:
Common questions
What does the work include?
We help your reviewers record corrections and restrict where the agent can retrieve them. Then we test whether those examples reduce repeat mistakes.
Do you fine-tune our model?
If it's worth testing after we review the problem. We try your best prompt and consider better examples or tool fixes first. We test an adapter on cases it wasn't trained on and check for new failures. The contract sets delivery and usage rights; the base model's license still applies.
What do we need to start?
Logs or conversation exports for one task, examples of good results, and a reviewer who can approve corrections. We agree data handling before you send anything. Remove unnecessary personal details.
How is success measured?
We test the existing and changed agent on the same task. Where the data is available, we compare completed tasks, human edits, handoffs, response time, and cost.
Related
Discuss how your team reviews agent replies.
Bring examples of mistakes and reviewer edits. We'll plan how to approve, save, and test corrections for one task.