Methodology

My AI agent implementation process

I do not treat agent work like a prompt-writing exercise. The job is to turn a repeated workflow into a system with the right context, the right review step, and a failure path that the team can live with.

1. Score the workflow before touching the model

I start by checking whether the workflow is repeated, painful, owned, and reviewable. If nobody owns it or nobody can judge the output quickly, it usually is not ready for an agent build yet.

2. Define the trust boundary

We decide what the system can observe, draft, recommend, and trigger. This is where approvals, auditability, and sensitive actions get handled on purpose instead of by accident.

3. Design the context, tools, and output shape

Useful systems need the right sources, not just a better prompt. I map what data is needed, what tools the system can call, what structure the output should follow, and what a weak result looks like.

4. Ship the narrowest useful version

The first release should prove the workflow, expose failure modes, and fit the team’s process. It does not need to be ambitious. It needs to be real enough to evaluate.

5. Measure where it fails

I care about weak drafts, missing context, routing mistakes, low-confidence cases, timeouts, and places where the review loop feels clumsy. Those are the clues that improve the system.

6. Tighten before expanding scope

Prompting, retrieval, tool logic, UX, cost controls, and approval rules get refined from observed usage. Most teams get more value from one hardened workflow than from several half-designed pilots.

What makes a workflow ready

The workflow already exists and somebody owns it.

The team already feels the pain often enough to care.

A human can tell quickly whether the output is good, weak, or unsafe.

The business value is obvious enough to justify proper design.

The system can improve a repeated preparation, drafting, research, classification, or recommendation step.

The trust boundary map

Observe

Read docs, tickets, CRM records, transcripts, product state, or other bounded context.

Prepare

Summarize, structure, classify, retrieve, and assemble context into a better starting point.

Recommend

Suggest a next action, draft a response, or rank likely options with confidence cues.

Act

Trigger an external action only when the approval rule, logging, and fallback path are explicit.

The production stack behind a useful agent

Model layer

Prompting, model selection, response shape, and cost/performance trade-offs.

Context layer

What information gets pulled in, how retrieval works, and how stale context is handled.

Tool layer

APIs, internal tools, search, structured actions, and the rules around when they are called.

Workflow layer

Trigger, state, output schema, confidence checks, and how the system fits the actual process.

Review layer

Approval UX, escalation logic, low-confidence handling, and who stays in the loop.

Observability layer

Logs, evals, traces, costs, fallback behavior, and the evidence needed to improve the workflow.

What I measure before I trust the workflow

Output quality and consistency
Time saved or throughput improved
Low-confidence or fallback frequency
Missing-context failures
Cost per useful completion
Where humans still have to repair the system manually

What usually goes wrong first

The workflow was not narrow enough.

The system did not have the right context, so outputs looked plausible but thin.

The review step existed in theory but felt too awkward in practice.

The team expected autonomy before they had enough evidence to trust the system.

No one was measuring failure patterns closely enough to improve the build.

Want me to look at a workflow?

Send the current workflow, the available inputs, who reviews the output, and what would make the result genuinely useful. I'll tell you whether it sounds strategy-ready or implementation-ready.