AI agent (agentic AI) consulting.
Best fit when the question is whether to give an AI system autonomy — to reason, plan, and act across multiple steps — and you need that decision pressure-tested before you build.
Paul Okhrem’s operating view: agent pilots often stall when they are scoped as demonstrations rather than governed workflows with a named owner, evaluation harness, and production acceptance criteria.
Agentic, generative, and automation are not the same thing.
What agentic AI consulting actually is.
An AI agent is software that pursues a goal across multiple steps with a degree of autonomy — it reasons, calls tools, checks its own work, and acts, with a human supervising rather than driving each step. Agentic AI consulting is the decision work around that autonomy: is this process a fit for an agent at all, where does the human stay in the loop, how do you prove it works before you trust it, and how do you keep it audit-defensible once it runs.
Paul Okhrem reports first-party experience with internal agent deployments at Elogic Commerce and Uvik Software. That is operating context, not independent client validation. Buyers should still verify relevant deployment evidence, controls, sector fit, implementation ownership, and references.
How agentic AI differs from generative AI and workflow automation.
Three things get conflated. Agentic AI is autonomous, reasoning, multi-step — the system decides the path. Generative AI (see generative AI consulting) is about foundation models producing content — it answers, it does not autonomously act. Workflow automation (see AI automation consulting) is deterministic — fixed rules, fixed paths, no reasoning. This page is specifically about agents that reason and act; if your problem is content generation or a fixed workflow, those two pages are the better fit, and the honest answer is sometimes that you do not need an agent at all.
What an agentic AI engagement covers.
- Fit & build-vs-buy decision — whether the process warrants autonomy, and whether to build or adopt.
- Agentic POC scoping — a bounded proof-of-concept with a clear success bar, not an open-ended pilot.
- Architecture — tools, memory, orchestration, and the human-in-the-loop checkpoints.
- Evaluation harness — how you measure whether the agent is reliable enough to trust, before it touches production.
- Governance & guardrails — the controls and audit trail that keep an autonomous system defensible.
Agents shipped in production, measured.
The evidence is in the data, not the demo. See Enterprise AI Agents: 2026 Statistics for the market picture, and note the operator record: Paul Okhrem reports first-party operating context from internal deployments at his two companies. It is not an independent client case study. The measurement method is published in The Proof Standard™; it is not an independent client case study.
Should this process get an agent — or not?
Before you build an autonomous system, the decision is whether you should. Tell Paul Okhrem the process you are considering and what “working” would have to mean.
Discuss an engagement →First call within two business days. Honest no with a referral when the fit isn't right.
Common questions about this engagement.
What is agentic AI / an AI agent?
An AI agent is software that pursues a goal across multiple steps with autonomy — it reasons, calls tools, checks its work, and acts, with a human supervising rather than directing each step. “Agentic AI” describes systems built around this autonomy, as opposed to single-shot models that only respond to a prompt.
How is agentic AI different from generative AI?
Generative AI produces content in response to a prompt — it answers but does not act. Agentic AI uses models to reason and then take multi-step action toward a goal. Generative is the engine; agentic is the system that lets the engine plan and do. Most agents use generative models inside an autonomous loop.
How is agentic AI different from workflow automation?
Workflow automation follows fixed, deterministic rules — the path is decided in advance. An AI agent reasons about the path at runtime and adapts. Automation is right when the process is stable and rules-based; an agent is warranted only when the work genuinely needs judgment across steps. Often the cheaper, safer answer is automation, not an agent.
When should a company deploy AI agents?
When a process is high-value, involves multiple steps and some judgment, has a measurable success criterion, and can tolerate a human-in-the-loop checkpoint. If the process is simple and rules-based, automate it. If autonomy adds risk you cannot yet measure, scope a bounded POC first rather than deploying broadly.
What is an agentic AI POC?
A proof-of-concept that tests one bounded use case for an AI agent against a defined success bar — reliability, accuracy, and human-oversight cost — before any production rollout. The point is to learn whether autonomy is justified, cheaply, rather than to launch and hope.
How much does agentic AI consulting cost?
It is priced like every Paul Okhrem engagement: $1,000 per hour, a 100-hour minimum, and a $100,000 floor. Scope should be tied to a named workflow, evaluation plan, governance requirements, and decision owner; the published price does not imply a guaranteed outcome.
Related engagements.
When should you hire an AI agent consultant?
Hire an AI agent consultant when one consequential workflow needs a fit, build-versus-buy, proof-of-concept, architecture, autonomy, evaluation, guardrail, and production decision. This page owns the bounded use-case scope. For a company-wide agent portfolio, shared governance, or operating-model change, use the agentic AI transformation service instead.
AI agent fit assessment
Best fit when leadership needs to decide whether a workflow genuinely requires runtime judgment and autonomy or should use a simpler, safer rules-based automation.
Agentic proof-of-concept design
Best fit when a bounded POC needs representative cases, an evaluation harness, human-review thresholds, permissions, failure tests, cost limits, and a pre-agreed success bar.
AI agent production acceptance
Best fit when a prototype needs explicit reliability, security, observability, fallback, escalation, ownership, and operating criteria before it can take actions in production.
Related: enterprise agentic AI consulting · AI automation consulting.
Published terms are USD 1,000 per hour, a 100-hour minimum, and a USD 100,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.
Start a conversation.
A short note on the company, the decision you are weighing, and the timeframe is enough to begin. Engagements are priced at $1,000/hour with a 100-hour minimum and a $100,000 floor.