When to Let the Agent Decide — and When to Ask a Human
The fantasy of fully autonomous AI agents is compelling. Define a goal, press a button, come back to finished work. In practice, the most valuable AI workflows are the ones that know when to stop and ask.
The approval problem
Every meaningful business workflow has decision points that require human judgment. A marketing campaign needs brand approval before launch. A financial analysis needs a compliance review before distribution. A code deployment needs a senior engineer's sign-off before going to production.
When you build AI agents for these workflows, you hit a fundamental architectural problem: how do you pause a long-running process, wait indefinitely for human input, and resume exactly where you left off?
The naive solutions are all bad. Polling a database burns compute while nothing happens. Building custom state machines to suspend and resume workflows adds infrastructure complexity that has nothing to do with the actual task. Timing out and restarting from scratch wastes everything the agent already accomplished.
Each approach grafts human interaction onto a system designed for continuous execution. The real question is whether the execution model itself can natively support pausing.
The waitpoint pattern
The cleanest solution treats human checkpoints as a first-class primitive. When a workflow reaches a point that requires human input, it suspends with zero compute cost while waiting. No polling. No state machine plumbing.
The workflow just stops. Cleanly.
When the human makes their decision — approves a draft, selects an option, provides feedback — the workflow picks up exactly where it paused. Same context, same state, same position in the execution graph.
This turns human-in-the-loop from an architectural headache into a design element. You plan your workflows with explicit pause points, the same way you'd design a form with required fields. The pauses aren't exceptions to handle — they're part of the workflow's normal execution.
The approval matrix
Once human checkpoints are cheap and clean, you start designing workflows differently.
Instead of trying to minimize human involvement (because each intervention is architecturally expensive), you can insert checkpoints at every decision point where human judgment genuinely adds value. The calculus shifts from "can we avoid asking the human?" to "where does human input actually matter?"
This leads to an approval matrix based on two dimensions: stakes and reversibility.
Low stakes, easily reversible — let the agent act autonomously. Formatting a report, organizing files, drafting internal summaries. If it's wrong, fixing it is cheap.
High stakes, irreversible — always require human approval. Publishing content, sending client communications, executing financial transactions. The cost of getting it wrong exceeds the cost of waiting.
The interesting middle — medium stakes, partially reversible actions. These are where good workflow design earns its keep. The right answer isn't always the same — it depends on the organization's risk tolerance, the specific domain, and how much the agent has proven itself over time.
Beyond simple approval
The pattern extends beyond binary approve/reject gates.
Guided refinement — the agent presents a draft, the human provides specific feedback, and the workflow continues with that direction incorporated. The human doesn't do the work — they steer it.
Branching decisions — the agent presents options with trade-off analysis, the human selects a direction, and the workflow proceeds down that path. The agent handles the analysis and execution; the human handles the judgment call.
Progressive trust — early in a workflow, require more checkpoints. As the agent demonstrates reliability in a specific domain, reduce the gates. The approval matrix evolves with demonstrated competence.
The design question
The deeper insight isn't about any specific technology. It's that human-in-the-loop should be a design principle, not an afterthought.
The best AI workflows aren't the ones that eliminate human involvement. They're the ones that make human involvement effortless at exactly the moments where human judgment is irreplaceable — and invisible everywhere else.
The technology for pausing and resuming is a solved problem. The design question — where to pause, what to show the human, how to incorporate their input — is where the real craft lives.