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reopt Handbook
reopt Handbook
Enterprise Project Architecture

Monorepo Foundation

Monorepo ArchitectureWorkspace DesignShared PackagesTurbo Pipeline

Apps and Delivery

Next.js PatternsVercel DeploymentCI/CD PipelineTesting Strategy

Agents and Operations

Agentic DevelopmentSkills EcosystemSecurity GovernanceMonitoring and Incident

Appendix

TemplatesReferencesUpdatesVerification
Handbook›Enterprise Project Architecture›Agentic Development
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Agentic Development

Use AI agents in enterprise development with context packs, scoped tasks, and review gates.

Key takeaways

  • Agentic development succeeds only when agents get a context pack, clear ownership boundaries, and human-reviewable changes.
  • A context pack carries goal and acceptance criteria, system map, constraints, commands, and known risks.
  • Scope agents to bounded work, keep write ownership clear in parallel runs, and demand changed file paths plus verification results.
  • High-risk changes to auth, payments, secrets, legal copy, or deployment config require explicit human approval and cannot bypass CODEOWNERS, CI, or release gates.
  • Codex teams encode org constraints in requirements.toml; Claude teams mirror the same policy in project settings and hooks.

Agentic development works when agents receive the right context, operate inside clear ownership boundaries, and produce changes that humans can review. It fails when agents are treated as unlimited autonomous developers without system knowledge.

Agent Workflow

Context Pack

SectionContents
GoalUser problem, acceptance criteria, non-goals
System mapRelevant apps, packages, APIs, ownership
ConstraintsStyle, security, performance, compatibility
CommandsTypecheck, tests, build, local server
RisksKnown edge cases and review focus

Task Scoping

  • Assign agents to concrete, bounded work.
  • Keep write ownership clear when multiple agents work in parallel.
  • Ask for changed file paths and verification results.
  • Review generated code with the same standards as human code.
  • Save recurring workflows as skills or templates only after they prove useful.

Governance

Agent outputs must pass normal CI and code review. High-risk changes involving auth, payments, secrets, legal copy, or deployment configuration require explicit human approval.

Multi-Agent Policy

PolicyRequirement
ContextKeep project instructions, context packs, and skills reviewed like code
IsolationUse branches or Git worktrees for long-running implementation tasks
PermissionsStart with least-privilege file, shell, network, and MCP access
VerificationRequire changed files and command results in the final agent report
MergeNever let agent output bypass CODEOWNERS, CI, or release gates

For Codex-based teams, place organization-wide constraints in managed configuration or requirements.toml; for Claude-based teams, mirror the same policy in project settings and hooks.

Related docs

Context Pack

Agentic MVP · Package the information an AI agent needs to build the MVP slice safely.

Prompts and Skills

Advanced Codex Usage · Design prompts, repository instructions, and reusable skills for repeatable Codex work.

Coding Orchestration

Vercel Enterprise AI Platform · Orchestrate repo-aware coding agents from issue intake to tests and pull request drafts.

Claude OS

Agentic MVP · Use Claude Code as an operating layer for MVP implementation.

Multi-Agent Workflows

Advanced Codex Usage · Split Codex work across bounded agents while preserving ownership and review.

Testing Strategy

Balance unit, integration, end-to-end, visual, and smoke tests for enterprise systems.

Skills Ecosystem

Build reusable agent skills, commands, and documents for repeated engineering workflows.

On this page

Agent WorkflowContext PackTask ScopingGovernanceMulti-Agent Policy