A production blueprint for one agentic workflow

The goal

Take one workflow and ship it as a reliable service.

Not a demo. Not a prototype. A production system that runs unsupervised, handles failures gracefully, and can be maintained by your team.

Minimal architecture

You need five components. No more, no less.

1. Orchestrator

The brain that coordinates the workflow.

**Steps** — defined sequence or DAG of operations

**Retries** — automatic retry with backoff for transient failures

**Timeouts** — kill long-running steps before they become expensive

**Budgets** — cap token spend per execution

**State** — track progress for resumption and debugging

2. Tools

The hands that interact with the world.

**Typed** — input and output schemas are explicit

**Validated** — bad arguments are rejected before execution

**Permissioned** — tools declare what they're allowed to do

**Observable** — every call is logged with timing and results

3. State

The memory that tracks progress.

**Inputs** — what started this execution

**Intermediate outputs** — results from each step

**Tool results** — what each tool call returned

**Trace metadata** — timing, tokens, model versions

4. Safety rails

The guardrails that prevent disaster.

**Allowlist tools** — the agent can only call approved tools

**Block risky actions** — some operations require human approval

**Redact secrets** — never log API keys or PII

**Budget enforcement** — stop before spending too much

5. Eval harness

The tests that prove it works.

**30–100 scenarios** — representative cases from real usage

**Automated scoring** — pass/fail criteria for each scenario

**Regression gate** — new changes can't break existing behavior

**CI integration** — evals run on every PR

The boring parts that matter

Agents fail. It's not about preventing all failures—it's about recovering gracefully.

Parsing and validation

Model outputs are strings. Parse them immediately. Validate against your schema. Reject garbage before it propagates.

Deterministic fallbacks

When the model fails, what happens? Define it explicitly. Return a safe default, queue for human review, or fail with a clear error.

Idempotent tools

Tools should be safe to retry. If a tool is called twice with the same input, the outcome should be the same (or at least not catastrophic).

Clear errors

When something fails, the error message should say what failed, why, and what to do about it. No stack traces in user-facing errors.

The path forward

If you want one workflow shipped end-to-end—with evals and safety rails—that's exactly what my Agent Build offer is for.

Book a call to discuss your workflow.