Scoped assignment
Each worker gets a small enough brief that progress is measurable and the output is easy to audit.
Parallel AI team workflows
Agents is the workflow idea behind running AI teammates in parallel: split the job, give each worker a scoped lane, run the lanes at once, and bring back structured results for the lead to integrate.
Each worker gets a small enough brief that progress is measurable and the output is easy to audit.
Independent work runs at the same time: source reading, UI polish, tests, implementation, review, or data gathering.
The lead receives findings, decisions, files touched, and residual risks in a format that can be merged.
A single orchestrator keeps the user’s goal coherent instead of letting agents produce disconnected fragments.
Find independent pieces: research, build, visual QA, code review, data entry, test writing.
Send bounded prompts with ownership, expected artifact, and stop conditions.
Ask for concise reports: changed files, confidence, blockers, and next actions.
The lead reads the work, resolves conflicts, verifies, and ships the single final result.
Build the web surface and keep layout responsive.
Work in the mobile or app codebase while preserving product behavior.
Exercise device flows, capture issues, and return exact failures.
Look for regressions, missing tests, wrong assumptions, and integration risks.
One worker reads architecture docs, another searches call sites, and another inspects tests. The lead gets a combined map before editing.
A designer-minded lane can refine copy and layout while a builder lane implements and a verifier lane checks screenshots.
Reviewers can independently look for security, UX, regression, and test gaps, then return findings with file references.
Named lanes preserve responsibility: app, HTML, backend, ADB, Supabase, docs, and review can each keep their own thread of work.
Workers gather project context before writing so the lead is not integrating guesses.
Each worker has a narrow deliverable, not a vague mandate to improve everything.
Useful reports include files, command output, screenshots, blockers, and residual risks.
Parallel workers can disagree; the lead chooses based on evidence and source truth.
Integration respects unrelated user changes and avoids broad rewrites.
The lead keeps the swarm attached to the user’s goal, not each worker’s curiosity.
The user receives a coherent outcome, not a pile of agent transcripts.