“Human in the loop” is often used as a general promise that people remain in control of AI.
That promise is not enough. Teams need to decide exactly where human judgment enters the workflow, what the reviewer sees, and how feedback reaches the agent.
Without a designed review loop, human oversight becomes one of two extremes:
- A person approves every minor action, eliminating the speed advantage of the agent
- The agent acts freely until a serious mistake reaches a customer or production system
The useful middle ground is risk-based review.
What does human in the loop mean for AI agents?
Human-in-the-loop AI is a workflow in which an agent performs work autonomously but pauses at defined checkpoints for human review, correction, approval, or escalation.
The checkpoint should match the consequence of the action.
For example:
- Reading an approved project document may require no additional approval
- Drafting a report may be allowed automatically
- Sharing the report with a client may require review
- Deleting a project or publishing a campaign should require explicit confirmation
The goal is not maximum human involvement. It is human judgment at the moments where it changes the outcome.
Why reviewing agent output is different
Traditional review workflows assume a person created the first draft and understands the reasons behind it.
Agent-generated work can be different:
- The output may be produced much faster and at higher volume
- The reviewer may not know which sources or instructions shaped it
- A revision may regenerate more than the reviewer intended
- Different agents may produce competing versions
- The cost of generation may be low, but the cost of human attention remains high
A good review workflow therefore needs more than a comment box. It needs clear states and responsibilities.
A five-stage review model
Stage 1: Draft
The agent creates the artifact inside a project with the relevant context and source material.
The draft should not be treated as approved simply because it is complete.
Stage 2: Submitted for review
The agent or user identifies the artifact as ready for review. The reviewer receives one focused request rather than a folder full of unexplained files.
A useful submission includes:
- What the agent was asked to accomplish
- The artifact to review
- Important sources or constraints
- The decision required from the reviewer
Stage 3: Human decision
The reviewer chooses a structured outcome:
- Approve
- Request changes
- Reject
- Escalate to another reviewer
Structured outcomes are more useful than vague reactions such as “not quite right.”
Stage 4: Agent revision
The agent reads the comments and proposes a revision. The revised output should remain connected to the previous version so the reviewer can see what changed.
Stage 5: Accepted project context
Once approved, the artifact becomes reusable project context inside the shared workspace. Future agents should use the accepted version rather than an abandoned draft.
This final stage is frequently missing. Teams review the work but fail to promote the decision into durable context.
Where to place review checkpoints
Use three questions:
1. Is the action reversible?
A private draft is easy to reverse. Sending an email to a customer is not.
2. Does the action cross a boundary?
Extra review is appropriate when work moves:
- From agent to customer
- From internal project to public channel
- From one organization to another
- From draft storage to approved knowledge
3. What is the cost of a wrong decision?
A typo in an internal note and an incorrect legal claim should not use the same approval policy.
How to avoid creating a review bottleneck
Review artifacts, not every tool call
A reviewer usually does not need to approve every file read or intermediate thought. Review the meaningful work product and high-risk external action.
Use defined acceptance criteria
Tell both the agent and reviewer what “good” means:
- Required sections
- Approved sources
- Brand or policy constraints
- Length or format
- Decision owner
Route work to the right expert
Not every output needs founder approval. Assign reviews by domain: engineering, brand, legal, finance, or client owner.
Separate feedback from approval
A comment is not the same as final approval. Use an explicit state so the team knows which version may be reused or published.
Measure reviewer effort
Track:
- Time to first review
- Number of revisions
- Most common rejection reasons
- Percentage approved on the first pass
- Reviewer minutes per accepted artifact
These metrics reveal whether the agent is reducing work or merely producing more drafts.
Example: Agent-generated market research
A founder asks an agent to research a new market.
- The agent creates a report using approved sources.
- The report is submitted to a product leader.
- The reviewer comments that the competitor section lacks pricing evidence.
- The agent updates only that section and attaches new sources.
- The reviewer approves the report.
- The approved report becomes context for a second agent creating an outreach plan.
The human is not manually conducting the research. The human is applying judgment to the decision-relevant parts of the output.
Example: Creative review
A creative agent generates a branded video concept.
- The agent produces a storyboard before expensive video generation.
- A creative director reviews narrative, claims, and brand fit.
- The agent revises the low-cost storyboard.
- Video is generated only after approval.
- The client reviews the finished output before publication.
This workflow places human control before the most expensive and externally visible actions.
Review is part of agent memory
When a reviewer says, “Never describe this product as cheap. Use accessible instead,” that decision should not disappear inside a single comment thread.
High-value feedback can become:
- A project instruction
- A brand rule
- An approved example
- A reusable checklist
- A constraint for future generations
This is how a review workflow improves future output rather than repeatedly correcting the same mistake.
How Agent Continuity supports human-agent review
Agent Continuity is designed around a loop in which agents create project artifacts, humans review them in context, and agents can address comments without moving the work to a disconnected system.
The goal is to preserve both speed and control: agents handle production, while people decide what becomes trusted project work. Agent Continuity is a product of Continuity (CONT’D).