Why does fresh context matter for coding agents?
Long sessions can accumulate stale instructions, unrelated discussion, and old implementation details. Fresh context keeps the agent focused on the current story.
Feature 02
Keep long AI coding work from turning into one drifting chat by giving each story its own bounded context window.
Why it matters
Long agent sessions drift. Hal keeps each story bounded so the agent receives the current task, relevant standards, and recent state—not every stale token from the whole feature.
Fresh context matters because long agent sessions can drift across old instructions, stale assumptions, and unrelated implementation details. Hal favors bounded stories so each implementation step receives the current task, relevant standards, and recent state without dragging the entire conversation forward.
A feature that begins cleanly can drift after several turns. The agent may carry old assumptions, respond to previous instructions, or blend unrelated implementation details into the next change.
That is how a simple sequence of stories becomes one large, hard-to-review diff.
Hal runs implementation as a loop of bounded stories. Each iteration can load the current story, relevant standards, and recent state without dragging the entire conversation forward.
hal validate
hal runThe agent gets enough context to act. The reviewer gets smaller checkpoints.
Review one story at a time. Confirm the changed files match the current story, the acceptance criteria are addressed, and the agent did not opportunistically modify unrelated parts of the repo.
| Hal does | Hal does not do |
|---|---|
| Structures PRD-native coding loops around planning, validation, implementation, reporting, and reviewable state. | Guarantee code quality, passing tests, delivery speed, revenue, rankings, or production readiness. |
| Helps supported engines work against smaller, reviewable units of work. | Replace developer review, QA, security review, or merge judgment. |
Before adopting this workflow, verify the current Hal source repository, install docs, pricing status, and machine-readable pricing. Check release notes and engine support before relying on Hal in production work.
Short answers before you put this into an agent workflow.
Long sessions can accumulate stale instructions, unrelated discussion, and old implementation details. Fresh context keeps the agent focused on the current story.
No. Hal can preserve inspectable state such as files, commits, reports, workflow state, and archives. The goal is to avoid carrying unnecessary chat history into every story.
No. It reduces context drift and improves reviewability, but developers still need to inspect the output before merging.
Keep exploring the pieces of a reviewable coding loop.
Turn product intent into stories, acceptance criteria, and reviewable state before an AI coding agent touches the repo.
Preserve progress, reports, workflow files, and loop state so AI coding work can be paused, inspected, and resumed.
Give each AI coding iteration the repo-specific commands, conventions, and review rules it needs to stay consistent.