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The decision framework: six questions about the task
Compare workflows on the task’s properties, not on a methodology’s brand.
- Ambiguity. Is the request already testable, or do requirements need to be clarified and written down? More ambiguity favors a spec and explicit clarification. GitHub’s Spec Kit documentation says its commands are meant to run in order, but only
specifyis strictly required beforeplan. Clarification, checklist and analysis steps are quality gates for meaningful ambiguity (Spec Kit, Agentic SDD). - Consequence and reversibility. Is an error cheap to detect and undo, or does the change touch security-sensitive, regulated or production behavior? Higher stakes call for stronger review. Anthropic’s playbook explicitly keeps human accountability for judgment-heavy decisions (Anthropic, AI-native SDLC playbook).
- Scope and duration. A small isolated fix needs a clear task and focused checks. Long-running work benefits from durable artifacts and intermediate verification.
- Coordination and audit. If work crosses people, sessions or automated triggers, committed specs, plans, tests, review findings and permission boundaries make handoffs inspectable.
- Control versus convenience. A managed runtime reduces integration work. An SDK or direct API gives your application more control over execution and state.
- Observed quality and cost. Measure quality, reliability, time, tool activity and corrections needed on representative work before broadening any workflow.
A workflow ladder: add process only when the task earns it
The rungs below are a synthesis of vendor guidance, not a validated named methodology. Move up only when the rung you are on is failing.
1. Clear, low-risk, bounded work
Give the agent the task, relevant project context and observable acceptance criteria. Ask it to make the change, run the relevant checks, and report what it did and what it could not verify. Then review the diff and the evidence. Don’t accept the agent’s own summary as proof: track the tests and commands that actually ran, errors, skipped checks and review findings.
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2. Ambiguous or multi-step feature work
Clarify the problem and constraints, write a specification, create a plan and tasks, analyze for gaps, implement in inspectable slices, then test and review. Spec Kit’s command sequence is one concrete implementation of this structure. Its own documentation treats several steps as optional gates, so the ceremony scales with the ambiguity (source).
3. Long-running or team-level lifecycle work
Anthropic’s playbook describes intent, specification, plan, implementation diff and tests, review findings and incident records as artifacts passed between lifecycle stages, with evaluation running continuously through implementation (source). Treat that as one vendor’s proposed model, not an industry standard. The practical idea is portable: keep decisions in version control so the next session, person or agent doesn’t have to guess.
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Be careful about extrapolating from long autonomous runs. OpenAI reported one Codex experiment of about 25 hours, roughly 13 million tokens and about 30,000 generated lines, and says plainly it was an experiment, not a production rollout (OpenAI Developers).
4. Repeated repository automation
For recurring jobs such as issue triage, CI investigation, status reports, documentation upkeep or test-coverage work, a repository-level workflow can fit. GitHub’s documentation describes read-only-by-default behavior, validation of declared write operations, and human review points. The feature is a public preview and subject to change (GitHub Docs). Declare narrow permissions and reviewable outputs.
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Tuning shared instructions: start from evidence
The VS Code guide puts it in one line: “Start with an observed project problem and a representative task.” (VS Code, Configure AI for your codebase). Its approach translates into a loop:
- Pick a repeated problem, such as wrong test commands, misplaced files or an unsuitable library.
- Choose a representative task with a clear success criterion and record current behavior.
- Make the smallest useful project-specific instruction change.
- Confirm the harness you use actually discovers the file.
- Repeat the task and compare outcomes.
Keep instructions to what agents can’t reliably infer. Excessive or conflicting instructions consume context without fixing the observed failure.
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Choosing a runtime: who owns the loop?
OpenAI’s agent documentation separates a managed agent harness, an SDK-controlled loop and direct model/API integration by who manages state, tools, runtime and deployment (OpenAI API, Agents). Ask where tools run, who holds state, and how much integration you are willing to build. GitHub’s workflow documentation lists the coding-agent engines it supports, so check that list against your tooling.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the empirical evidence does and doesn’t say
- Task mix matters. A 2026 arXiv preprint analyzing 7,156 pull requests across five coding agents reports acceptance varying by task category, with no agent leading every category (Comparing AI Coding Agents). In that dataset, documentation PRs were accepted at 82.1% versus 66.1% for new features. Claude Code reached 92.3% on documentation and 72.6% on features, and Cursor 80.4% on fixes. These describe that dataset only; they are not forecasts for your team or a tool recommendation.
- Speed can outrun understanding. A separate preprint on spec-driven development in a project-based learning course found agent use raised implementation throughput but tended to let students proceed without fully understanding the code. The authors stress comprehension checks and instructor feedback (arXiv). It’s an educational setting, so don’t apply it directly to professional teams. It does argue for a human understanding gate.
- No head-to-head trial crowns a methodology. The workflow guidance is vendor-authored, and the empirical studies have bounded contexts. Treat this article as a decision framework, not a causal ranking.
The Bottom Line
Start at the lightest rung: a clear task, acceptance criteria, checks and a diff review. Climb to specs, plans and gates only when ambiguity, stakes or coordination demand it, and keep a baseline so you can tell whether the extra process helped.
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