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CTOs and engineering leaders

Standardize agentic engineering across teams with shared AGENTS.md conventions, skills, hooks and managed settings; choose models and gateways; make evals and observability policy; review agent security; control cost; and measure productivity honestly.

Checked 13 Oct 202620 min read14 prompts1 skill1 hook2 routines

For CTOs, VPs of engineering, heads of platform and engineering managers

One safe, measured way of working with coding agents, across every team

  • One set of agent conventions in every repository: AGENTS.md, shared skills, team hooks and REVIEW.md
  • Organization-wide guardrails delivered through managed settings that developers and repositories cannot override
  • A model and gateway plan with tiers, version pins, budgets and a named owner
  • Every AI feature shipped with an eval suite, traces, a cost budget and a kill switch
  • Productivity reported with lead time, change fail rate, rework and cost per change, not lines of code
  • A vendor risk file for each AI provider: data use, retention, residency, certifications and exit plan
Works withClaude CodeOpenAI CodexGitHub CopilotCursorLiteLLMLangfusepromptfooOpenTelemetry
On this page
  1. Where AI helps in this job
  2. Start in 15 minutes
  3. The tools engineering leaders use
  4. Members only: Workflow: standardize agentic engineering across teams
  5. Members only: Workflow: model and gateway strategy
  6. Members only: Workflow: evals and observability as policy
  7. Members only: Workflow: a security review of your coding-agent rollout
  8. Members only: A skill for engineering leaders
  9. Members only: Guardrails and hooks
  10. Members only: Routines that run without you
  11. Members only: Prompt ladder for engineering leaders
  12. Members only: Measure it
  13. Members only: Risks and what not to automate

Your engineers are already using coding agents, and each team has set them up differently. Your job is to make one way of working the default: the same conventions in every repository, guardrails that hold whatever an individual configures, a deliberate model and vendor strategy, and numbers you can defend. This page gives you the kit, the policy files and the measurement plan.

Where AI helps in this job#

TaskWhat AI does wellWhat you still own
Standardizing agent useDrafts AGENTS.md templates, skills and settings from your best repositoriesThe standard, and enforcing it
Guardrails and policyTurns a written policy into managed settings, permission rules and hooksWhat is allowed, and the exceptions
Model and vendor choiceBuilds comparison tables from vendor docs and runs your eval set against each candidateThe decision, the contract and the exit plan
Cost controlSummarizes spend by team, model and tool and flags outliersBudgets and trade-offs
Productivity measurementPulls lead time, change fail rate and rework from Git and CI dataWhat the numbers mean, and what you tell the board
Evals and observabilityGenerates eval cases from real traffic, writes graders, reads tracesPass criteria and the release decision
Architecture decisionsDrafts decision records with options and consequencesThe decision
Agent security reviewsBuilds threat models and control checklists for agent rolloutsAccepting the residual risk
OnboardingExplains the codebase to new hires through skills and AGENTS.mdMentoring and judgment

The traps at this level are numbers and claims. Vendor benchmarks rarely match your codebase, activity metrics such as lines accepted rise whether or not outcomes improve, model aliases move under you, and agent loops can burn budget quietly. Self-reports are unreliable too: in METR's 2025 study, experienced developers believed AI had made them about 20% faster when they were measured 19% slower. Decide on evidence from your own repositories.

Start in 15 minutes#

These work in any agent that can read a repository (Claude Code, Codex, Cursor, Copilot) or, for the last one, in any assistant with web search.

Prompt

Audit one repository's agent setup

Level 2Any assistant
Audit this repository's setup for coding agents. Do not change anything. Check and report pass or fail with the evidence for each:
1. AGENTS.md or CLAUDE.md exists and names the install, build, lint, type check and test commands.
2. Those commands actually run. Try lint and tests, and report how long the tests take.
3. Agent settings deny reading .env files and secret folders, and no rule approves every shell command.
4. CI runs lint and tests on every pull request.
5. MCP server configuration contains no credentials.
End with the three changes that would help agents most.

Good output cites files and command output for every line. Run it on three repositories from different teams; the differences become your standardization backlog.

Prompt

Draft an architecture decision record

Level 1Any assistant
Turn these notes into an architecture decision record with the sections Context, Options considered, Decision, Consequences (good and bad) and Revisit when: [PASTE NOTES]. Keep it under one page, and list any assumption the notes do not support.

Good output separates what was decided from what was assumed. Check the Consequences section with the engineer who will live with the decision.

Prompt

Vendor comparison with a source for every cell

Level 2Any assistant
Build a comparison table of [AI CODING TOOLS OR MODEL PROVIDERS] for an engineering organization of [SIZE] in [REGION]. Rows: training on our data by default, data retention and zero-retention options, data residency, SOC 2 and ISO 27001, SSO and SCIM, audit logs, admin policy controls, usage analytics, and pricing model.
Fill each cell only from the vendor's own documentation, trust center or pricing page, and put the URL in the cell. Write "not found" instead of guessing.

Good output has a URL in every filled cell and some "not found" cells. Open every URL behind a cell that would decide the choice.

The tools engineering leaders use#

  • Claude Code: managed settings delivered by file, MDM or the claude.ai admin console; an organization-wide CLAUDE.md; MCP server allowlists; plugin marketplace restrictions; an analytics dashboard on Team and Enterprise (active users, sessions, pull requests and lines with Claude Code); OpenTelemetry export of sessions, tokens, cost, commits and pull requests; and Claude apps gateway, a self-hosted gateway built into the claude binary with identity-provider sign-in and policy per group.
  • OpenAI Codex: managed configuration through /etc/codex/requirements.toml or MDM, which constrains approval policies and sandbox modes, plus Agent Security settings in the admin console. Codex reads AGENTS.md.
  • GitHub Copilot: enterprise AI controls for agents, clients and MCP servers, and a Copilot usage metrics dashboard and API. GitHub calls its own lines-of-code and ROI figures directional estimates.
  • Cursor: enforced team rules that members cannot turn off, team hooks and an MCP allowlist on Enterprise plans, and admin and analytics APIs.
  • Gateways: Claude apps gateway for Claude Code, or a general LLM gateway such as LiteLLM for keys, budgets and routing across providers.
  • Evals and observability: promptfoo and Inspect for evals, Langfuse and the OpenTelemetry GenAI conventions for traces.

Workflow: standardize agentic engineering across teams

Every repository should give every agent the same three things: context it can trust, limits it cannot argue with, and a check that tells it when it is done. Ship that as a kit, enforce the parts that matter centrally, and roll it out team ...

Members only below this line

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  • 11 prompts
  • 1 skill
  • 1 hook
  • 2 routines

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Also in the members part

  1. Workflow: model and gateway strategy
  2. Workflow: evals and observability as policy
  3. Workflow: a security review of your coding-agent rollout
  4. A skill for engineering leaders
  5. Guardrails and hooks
  6. Routines that run without you
  7. Prompt ladder for engineering leaders
  8. Measure it
  9. Risks and what not to automate

Sources

Checked against these on 13 Oct 2026.

  1. Set up Claude Code for your organization (opens in a new tab)code.claude.com
  2. Deploy managed settings (Claude Code) (opens in a new tab)code.claude.com
  3. Manage Claude Code plugins for your organization (opens in a new tab)code.claude.com
  4. How Claude remembers your project (CLAUDE.md and AGENTS.md) (opens in a new tab)code.claude.com
  5. Claude Code hooks reference (opens in a new tab)code.claude.com
  6. Monitoring Claude Code with OpenTelemetry (opens in a new tab)code.claude.com
  7. Track team usage with analytics (Claude Code) (opens in a new tab)code.claude.com
  8. Manage costs effectively (Claude Code) (opens in a new tab)code.claude.com
  9. Run Claude Code through a gateway (opens in a new tab)code.claude.com
  10. Model configuration (Claude Code) (opens in a new tab)code.claude.com
  11. Use Claude Code in the cloud (GitHub access in cloud sessions) (opens in a new tab)code.claude.com
  12. Codex managed configuration (opens in a new tab)learn.chatgpt.com
  13. Codex: custom instructions with AGENTS.md (opens in a new tab)learn.chatgpt.com
  14. GitHub Copilot usage metrics (opens in a new tab)docs.github.com
  15. Cursor rules (team rules and AGENTS.md) (opens in a new tab)cursor.com
  16. DORA's software delivery performance metrics (opens in a new tab)dora.dev
  17. DORA: 2025 State of AI-assisted Software Development (opens in a new tab)cloud.google.com
  18. DX: measuring AI code assistants and agents (opens in a new tab)getdx.com
  19. METR: early-2025 AI and experienced open-source developer productivity (opens in a new tab)metr.org
  20. Field experiments on AI coding assistants (Management Science) (opens in a new tab)doi.org

Related

GuidePrompting, from easy to hardHow the 14 prompts on this page workGuideSkills: teach an agent a procedure once, reuse it everywhereHow the skill on this page worksGuideHooks: guardrails that run every timeHow the hook on this page worksGuideRoutines: scheduled agents that open pull requestsHow the 2 routines on this page work