Agentic AI Development Loop

Use this playbook to turn a candidate task into a reliable workflow.

1. Frame the outcome

Name the user, goal, inputs, deliverable, and non-goals.
Define observable success and unacceptable failure.
Decide whether a direct prompt or deterministic code is already sufficient.

2. Establish a baseline

Implement the simplest one-call or fixed-pipeline version. Record quality, latency, cost, and failure traces. Complexity must earn its place by beating this baseline.

3. Build the eval set

Include typical cases, boundary cases, known failures, adversarial content, missing data, tool outages, and permission denials. Store expected behavior and severity, not only expected wording.

4. Decompose the workflow

For each component define:

input → decision/transformation → output contract → validator → failure path

Keep validation, authorization, calculations, and stable business rules in deterministic code.

5. Add one capability at a time

Reflection when output can be checked and repaired.
Tools when exact, current, or private information/action is required.
Planning when steps must adapt to observations.
Multiple agents when specialization or parallelism measurably helps.

Run the same evals after every addition.

6. Define the autonomy envelope

Tool allowlist and least-privilege credentials
Read-only defaults and sandboxing
Maximum calls, iterations, time, and spend
Confirmation for sending, publishing, purchasing, deleting, or mutating important data
Explicit escalation and stop conditions
Trace logging with sensitive-data controls

7. Inspect failures

Tag every failure by stage and cause. Prioritize frequent, high-impact failures with a clear remedy. Fix the component rather than adding vague instructions to the system prompt.

8. Prove and ship

Run outcome, component, safety, latency, and cost evaluations.
Test tool failures and approval gates.
Compare against the baseline.
Canary the workflow and monitor drift.
Keep a rollback path and version prompts, tools, models, and eval data.

Pull-request evidence

Outcome:
Baseline:
Workflow change:
Eval dataset/version:
Quality before → after:
Latency/cost before → after:
Safety and permission tests:
Known risks:
Rollback:

Agentic AI Development Loop