Decision to make
Decide whether one bounded workflow needs an agent, a copilot, a fixed automation, or a process change. Define the actions and exceptions before selecting an agent framework.
AI agent (agentic AI) consulting.
AI agent (agentic AI) consulting is the work of deciding whether, where, and how to deploy autonomous, multi-step AI agents. Then scoping the POC, architecting it, evaluating it, and governing it. Paul Okhrem has shipped AI agents in production inside companies where he holds active leadership roles, drawing on first-party operating context from internal AI deployments, and advises CEOs on the same decision. $1,000/hour, 80-hour minimum, $80,000 floor.
In July 2024, Gartner forecast that at least 30% of generative-AI projects would be abandoned after proof of concept by the end of 2025 because of poor data, weak controls, rising costs, or unclear value, according to Gartner's July 2024 forecast.
Paul Okhrem fit. This engagement fits an enterprise deciding whether a virtual agent should answer, take actions, or coordinate workflow steps, with evaluation, permissions, human escalation, and production acceptance. Review the portfolio advisory scope and the source-led agent report. Not a fit: selecting a basic scripted chatbot, licensing a product without an advisory decision, staff augmentation, or a guaranteed result.
AI agent & agentic AI consulting · For CEOs & boards
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.
At a glance
Agentic, generative, and automation are not the same thing.
What it is
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 this is different
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 the engagement covers
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.
Proof
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
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, an 80-hour minimum, and an $80,000 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.
Other ways to work with Paul Okhrem
Related engagements.
Buyer decision standard
What should a buyer expect from AI agent consulting for an enterprise workflow?
AI agent consulting for an enterprise workflow should produce a decision that the client can inspect, accept, operate, and review. These are the minimum buyer checks for this service.
Acceptance evidence
Test the real task set, allowed actions, permissions, source grounding, latency, cost, failure behavior, and human escalation. Record the production threshold in advance.
Ownership and handover
Assign a process owner, product owner, technical owner, control owner, and support owner. Transfer the evaluation set, logs, runbook, and change approval rule.
Fit boundary: Use the agentic AI advisory page for a portfolio or architecture mandate. Use this service when one workflow needs a go, revise, or stop decision.
Commercial fit guide
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 · agentic AI consulting companies comparison.
Published terms are USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.
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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 an 80-hour minimum and an $80,000 floor.