Pragmatic AI Adoption

If your team ships features, captures learning in docs, and pays off technical debt each sprint, you compound returns. We provide a simple checklist you can run on repeat.

Ship three outcomes with one effort

The fastest way to get durable AI leverage is to braid three outcomes into every sprint:

• Product value: A shipped feature, workflow, or improvement that a user benefits from today.
• Learning: A short, durable write-up capturing what worked, what failed, and why.
• System improvement: A small upgrade to tooling, data quality, evaluation, or infra that makes the next sprint cheaper and faster.

You don’t need big bets or moonshots. You need a weekly loop that compounds.

The weekly loop (repeatable)

1. Pick one outcome users will feel

   • Example: “Reduce onboarding ticket handle time by 20% with an agent-assisted checklist.”
   • Write the success metric and verification method up front.

2. Ground with the smallest context that can work

   • Identify the single source of truth (runbook, API, dataset) and the exact fields/commands you’ll use.
   • Avoid broad search on the first iteration.

3. Design the minimal loop

   • Plan → Ground → Act → Check → Summarize.
   • Prefer deterministic tools and idempotent operations. Add a hard budget (steps, time, tokens).

4. Ship behind a flag

   • Gate with environment config or a feature flag. Start with internal users.

5. Instrument ruthlessly

   • Log prompts, tool calls, outputs, checks, and final artifacts as a transcript ID.
   • Capture latency, token usage, success/fail reason.

6. Write the 400–700 word learning

   • What hypotheses did you test? What failed? What’s your new belief?

7. Pay one debt item

   • Stabilize a flaky tool, add a missing check, clean up a prompt, or improve chunking.

8. Demo + decide

   • Show the artifact, review metrics, decide whether to widen the flag, and queue the next debt item.

Definition of Done (copy/paste)

• A user-facing change is merged and deployable (flagged is fine)
• Success metric captured with before/after (screenshot, table, or log sample)
• Transcript or run summary attached (prompts, tools, outputs, checks)
• 1–2 paragraph learning added to the knowledge base
• Exactly one small debt ticket closed (tooling, eval, data, or prompt hygiene)

Templates you can steal

PR template (agent/AI work)

Title: [Feature/Workflow] — impact + guardrails

Problem
- Who benefits and how will we know?

Approach
- Plan → tools → checks → budgets
- Flags and scopes (what can it touch?)

Verification
- Expected diffs / artifacts
- Unit/integration checks and manual spot-check steps

Observability
- Log keys, transcript ID format, dashboards/links

Risks & Rollback
- What would we revert and how?

Run summary (paste in your docs)

Run: agent-onboarding-checklist
Date: 2025-06-22
Owner: @you
Change: Assisted checklist reduces handle time

Inputs
- Ticket type: onboarding
- Data: CRM v3, fields [company_size, plan, region]

Outcomes
- Lead time: 11m → 6m (median)
- Rework rate: 23% → 12%
- Cost/run: $0.08 tokens + $0.01 infra

Notes
- Two failures due to missing region mapping; added a guard + fallback to manual.

Debt ticket (tiny, done-in-a-day)

Title: Add 2xx/shape check to CRM API tool

Scope
- Only tool: crm.getCustomer(id)
- Add schema check for { id, plan, region }

Why now
- 12% of failures were schema drift; this removes a class of silent errors.

AI-specific adoption guidelines

• Autonomy levels: Start at Draft‑only → Constrained execution → Supervised writes → Trusted tasks. Graduate per workflow, not globally.
• Verification first: Prefer exact predicates (tests, schemas, allowed diffs). Use LLM self‑checks as advisory, fail closed when uncertain.
• Tooling quality: Make tools idempotent with dry‑run modes and explicit scopes (file globs, resource allowlists).
• Context discipline: Pull just enough context; resist “search everything.” Add retrieval later with evaluations.
• Budgets: Enforce max steps, tokens, and wall clock time. Log budget exits distinctly.
• Observability: Every action ties to a transcript ID. Keep structured JSON logs and one-click drilldowns.
• Privacy + provenance: Do not send secrets; redact by default. Tag data sources and versions in outputs.

What to build first (high signal)

• Template PRs: Safe, repeatable edits across many files with tests.
• Operational runbooks: Restart/check/service sanity flows with clear success criteria.
• Structured reporting: Weekly tables from APIs with deltas and short notes.
• Context packers: Small functions to assemble the exact inputs a workflow needs.

Avoid first: open‑ended research, subjective copywriting, or anything requiring broad crawling.

Metrics that prove compounding return

Track by workflow. Promote when the numbers stabilize for two consecutive weeks.

RACI for a small team

• Product — Accountable for outcome metric and flag rollout plan.
• Engineer — Responsible for tools, checks, and guardrails.
• Data/ML — Consulted on evaluation design and drift alerts.
• Support/Ops — Informed; provides feedback from real use.

4‑week rollout plan

Week 1: Ship the smallest loop behind a flag, add transcript logging, define success metric.

Week 2: Cut rework in half with one debt fix, publish the first learning note, widen to 3–5 internal users.

Week 3: Add an evaluation or schema check, reduce latency, and enable supervised writes for the single workflow.

Week 4: Lock in dashboards, document runbooks, and graduate that workflow to “trusted” if numbers hold.

Common pitfalls (and fixes)

• Big‑bang projects → Replace with small weekly loops that ship.
• Prompt spaghetti → Centralize prompts, add owners, and prune monthly.
• Ambiguous success → Define the metric up front and report it in every demo.
• No audit trail → Store transcripts with stable IDs and link them in PRs.
• Skipping verification → Add one exact predicate now; fancy evals can wait.

A one‑page weekly checklist

• Define 1 user‑felt outcome and the success metric
• Assemble minimal context and deterministic tools
• Implement the smallest loop with budgets and a transcript ID
• Ship behind a flag to a small audience
• Capture before/after and a short run summary
• Close one debt ticket that reduces future rework
• Demo and decide: widen, refine, or stop

See also

• Beyond autocomplete → agent loops that do real work: /blog/beyond-autocomplete
• Build a RAG search for your blog: /blog/rag-for-your-blog-open-source
• A reference coding agent architecture: /blog/build-a-coding-agent-ts

Closing thought

Pragmatic AI adoption is disciplined shipping. Tie product value, learning, and system improvement together every week, and the compounding starts to take care of itself.