Coding agents compress implementation time. They do not remove dependency order, ambiguous requirements, missing capabilities, or the need for evidence that a change works. In fact, higher throughput makes those constraints more important: an agent can accumulate the wrong changes much faster than a person can review them.
Throughput moves the bottleneck
In ordinary development, it is tempting to keep pushing when a task exposes a missing prerequisite. Add half of the feature, patch around the gap, leave a note, and plan to clean it up later. With an agent in the loop, that creates a mixed working state: several intentions, incomplete assumptions, and unrelated edits all compete for the same context.
The implementation may look busy while the system is no closer to a trustworthy result. The actual work is no longer “finish the feature.” It is “establish the condition that makes the feature possible.”
Treat the blocker as the next deliverable
A blocker-first loop is deliberately simple:
- 01Return to a clean baseline.
Remove unrelated partial work so the repository has one clear intention.
- 02Name the prerequisite.
Turn “this is blocked” into a concrete capability, decision, or failing contract.
- 03Solve it as its own change.
Implement the smallest complete prerequisite without smuggling in the original feature.
- 04Prove it.
Use tests, a build, or an observable system behavior to establish a new baseline.
- 05Resume the original task.
Start again from a repository whose new capability is explicit and reusable.
Why this works unusually well with agents
Bounded context
Each change has one goal. The agent does not need to preserve a growing pile of partially valid assumptions.
Failure attribution
When verification fails, the cause belongs to the prerequisite change—not to three overlapping features.
Reviewable history
The resulting commits explain the system’s dependency structure instead of hiding it inside a large diff.
Reusable capability
A clean prerequisite can unblock more than the task that first revealed it.
Safety rails make the speed usable
Blocker-first sequencing keeps the change coherent. Delivery automation determines whether that coherent change is safe to trust. Fast tests, immutable build artifacts, production-like configuration, progressive rollout, and automatic rollback convert agent throughput into operating leverage instead of review debt.
The point is not to surround every change with ceremony. It is to make correctness cheaper to demonstrate than to debate. An agent should be able to move quickly because the system supplies rapid, objective feedback.
A good repository can answer three questions at any moment:
- What single change are we attempting?
- What evidence will prove it works?
- What unresolved prerequisite could make that evidence meaningless?
Not every uncertainty is a blocker
This pattern is for dependencies that invalidate downstream work: a missing interface, an unclear product decision, a broken build, absent test infrastructure, or a capability the feature genuinely requires. Local, reversible uncertainty is different. If a reasonable assumption can be tested cheaply, make it explicit and continue.
The distinction is structural: would continuing create useful evidence, or only more state that must be untangled later? If progress depends on an unproven invariant, solve the invariant first.
The practical result
Blocker-first work often feels slower for the first hour because it refuses visible but invalid progress. Over the life of a change, it is faster. The repository stays legible, verification stays local, and both humans and agents can resume work without reconstructing a hidden chain of compromises.