AI Workflow & Agentic AI
One AI workflow or agent live in production, measured and owned.
Why most pilots stall
You have run AI experiments that demo well and stall before production. You want one workflow or agent that solves a real problem and earns its place.
Where the gap opens up
It only works on the happy path
The demo shines on a clean input. Real traffic, flaky tools, and unattended 2am runs are where it quietly falls over.
Nobody can trust it unattended
With no evals, guardrails, or observability, there is no way to know it stayed in policy, and no way to catch it when it drifts.
There is no owner to hand it to
It lives on the laptop of whoever built it, so it never crosses into a system your team can actually run.
What this engagement adds
The work that turns a promising demo into something your team can run, trust, and own.
Live in production
Running in your environment, not a notebook.
Eval-gated
A scored suite blocks bad releases.
Guardrailed
Input and output checks keep it in policy.
Observable
Traces and metrics on every run.
Cost-controlled
Token and tool budgets, capped and alerted.
Owned by your team
A runbook and handover, so it is yours.
Workflow or agent
A workflow follows your steps. An agent decides its own.
Both put AI to work in production. The difference is who controls the path, and that is what makes each one the right tool for a different job.
AI Workflow
A fixed path you define
You lay out the steps, and the model fills the parts that need language or judgment. The path is the same every run, so it stays predictable, auditable, and easy to reason about.
Deterministic, repeatable steps
Easy to test and audit
Lowest cost and latency
Best for
Well-understood, repeatable processes where the steps rarely change.
AI Agent
A goal it works out for itself
You give it a goal and the tools to reach it. The model plans, picks the next step and the right tool, and loops until the goal is met. It is more capable on open-ended work, and it needs guardrails to stay safe.
Plans and chooses its own steps
Handles open-ended, branching work
Needs guardrails and review
Best for
Tasks where the path is not fixed and judgment changes what happens next.
Most real systems are a blend of the two. We use the least autonomy that does the job, and add agency only where it earns its keep.
The agentic pipeline
Anatomy of an agent in production.
Click any stage to see what it does, and what makes it safe to run unattended.
Stage 1 of 6 · Where the run starts
Trigger
A webhook, a schedule, an inbound ticket, or a user message kicks off a run. Our agentic approach is event-driven by design, so the same pattern scales from a single trigger to thousands of concurrent runs as you expand. Every input is validated and each run is made idempotent, so the same event never fires twice.
Agentic patterns, used where they fit
Tools
Typed actions the agent can take in your systems.
Retrieval
Grounding answers in your own knowledge.
Guardrails
Checks that keep outputs safe and in policy.
The production bar
What changes when a demo has to run for real.
Handles the happy path. The edges and the unattended runs are where it breaks.
A demo gives you
- Works on the happy path
- Looks impressive in a meeting
- One prompt, one good answer
Useful for proving the idea. None of it survives a bad input, a flaky tool, or a 2am run.
Production adds
0 of 7 in place- Missing
Evals
A scored test suite that catches regressions before release.
- Missing
Guardrails
Input and output checks that keep it inside policy.
- Missing
Monitoring and observability
Traces, logs, and dashboards for every run.
- Missing
Cost control
Token and tool budgets, with caps and alerts.
- Missing
Error handling
Retries, fallbacks, and graceful failure paths.
- Missing
Human in the loop
Review and approval where the stakes are high.
- Missing
Runbook
A handover doc so your team operates it without me.
Measured to a standard
Built to a standard, measured against it.
0%
of AI-generated code carries a known security flaw.
Reported across independent security analyses. It is the reason production needs evals, guardrails, and review, the layer a demo skips.
Reliability
Successful runs over total runs.
Eval pass rate
Quality bar the suite enforces before release.
p95 latency
95% of runs finish under this.
Cost per run
Budgeted, capped, and alerted.
Illustrative of the kind of targets we agree before the build. The exact numbers depend on your workflow and we set them together. They describe what we aim to hit once it runs in production.
What you get
One useful win in production, measured and owned.
I design and deliver a single AI workflow or agent that solves a real business problem and earns its place in production. Common starting points are an agentic assistant over your internal knowledge, customer support acceleration, or operational triage.
An AI workflow or agent running in your production environment
A baseline and a target KPI agreed before the build
Agentic patterns where they fit: tools, retrieval, and guardrails
Reliability, safety, and observability built in
A runbook and handover so your team owns it
This is a fit if
- You have an AI experiment that demos well and stalls before production.
- You can name one workflow where a win would matter.
- You have a system of record the agent can read from and write to.
- Your team wants to own what gets built.
Probably not the right fit yet
- You have not decided where AI should help yet.
- You want a research prototype with no production target.
- There is no owner on your side to hand it to.
Best for: Teams that want one useful win in production, measured and owned.
How it works
Three steps from idea to production.
Scope one workflow
We pick a single high-value workflow or agent and agree the baseline and the KPI it has to move.
Build it for production
I design and build it with agentic patterns where they fit, and reliability, safety, and observability designed in, joining your team or augmenting it.
Ship and hand over
We put it live, measure it against the KPI, and hand you a runbook so your team owns it.
When it fits how we work, this can include a hands-on enablement session so your team builds the muscle alongside me.
The outcome
One AI workflow or agent running in production, measured against a real KPI, and owned by your team.
Book an AI Opportunity Review
Book a 30-minute
AI Opportunity Review.
This call is for leaders who want to find where AI can create operational value, what is realistic in their environment, and what to deprioritize now. If we are a fit, I will propose the next step. If we are not, I will tell you directly.
Write to Bruno
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