Agentic AI consulting and AI agent advisory
Paul Okhrem provides agentic AI consulting for enterprise leaders at USD 1,000 per hour, with an 80-hour minimum. His AI agent consulting covers workflow fit, architecture, permissions and production acceptance. He defines which actions an agent may take, how people review them and when the company should stop.
Price: USD 1,000 per hour, 80-hour minimum, USD 80,000 floor. Delivered personally by Paul Okhrem. Review the published terms; engineering, software and external specialists are separate unless explicitly included.
What is agentic AI consulting?
Agentic AI consulting decides whether software should choose and perform steps toward a goal. It defines useful autonomy, allowed actions and tests before release. Paul Okhrem reviews those decisions with business and technical leaders. The work can cover one workflow or shared controls across several agents.
An agent may read approved data, choose a next step and use a tool. That power needs limits. A demonstration alone does not prove that the system can operate safely or at an acceptable cost.
Agentic AI services: what is included
AI agent consulting services from Paul Okhrem cover decisions and control, not code delivery. The written scope identifies the workflows and outputs.
- Use-case fit: compare an agent, a copilot, fixed automation and a process change.
- AI agent strategy: choose which workflows to address first, in which order, with named owners.
- Architecture review: assess models, data retrieval, permissions, task coordination and testing.
- Vendor and partner selection: compare platforms, model providers and implementation partners against the same criteria.
- Production acceptance: define the tests, approvals and monitoring required before release.
- Portfolio ownership: agree a separate executive mandate when several agents need continuing leadership.
Each output must support a decision: proceed, revise or stop. A test plan names representative tasks, pass thresholds and failure cases. An acceptance checklist names who can approve launch and who will operate the system afterward. Your team or chosen partner builds and runs the agent.
When should a company use an AI agent?
Use an agent when inputs vary and a multi-step task needs choices within clear limits. Confirm the business case before choosing tools.
- Before funding an agent: a team needs rules for actions involving customer, financial or system data.
- Before moving a pilot into production: demos work, but tests, monitoring or ownership are incomplete.
- Before increasing autonomy: a regulated firm must decide which steps still need human approval.
Not a fit: a stable rules-based workflow that already meets the need, or an agent build without a business case. These are design scenarios, not claims of completed client projects.
Agentic AI, generative AI and fixed automation
Choose the simplest approach that meets the task and control requirements. More autonomy adds testing and operating work.
| Approach | Typical behavior | Useful starting point |
|---|---|---|
| Generative AI | Produces text, code or other output; it can also support an agent | Drafting, search or analysis with a person reviewing the result |
| Fixed automation | Follows predefined rules and paths | Stable processes with known steps and structured inputs |
| Agentic system | Selects steps and uses tools within allowed boundaries | Variable tasks that justify controlled decisions while running |
Many tasks need drafting, search or analysis with a person in the loop rather than an agent. That work is covered by generative AI consulting.
What agentic AI can deliver, and how to measure it
An AI agent creates value when it completes useful work at an acceptable error rate and cost. Possible gains include fewer handoffs and shorter cycle times.
Time saved becomes value only when the company removes cost, uses the added capacity or increases revenue. Measure each agent against the current process:
- Before the pilot, record volume, cycle time, error rate and cost per case.
- After release, compare the same measures over a fixed window.
- Count model usage, integration, monitoring, human review and incident handling.
Paul applies the Proof Standard to each claim: baseline, intervention, measurement window and other changes that could affect results. If the agent cannot beat the baseline, stop it or redesign it.
For adoption and outcome figures with sources, see the enterprise AI agent adoption data. Survey adoption is not evidence of a return on your project.
Gartner predicts that 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024, and that at least 15% of day-to-day work decisions will be made autonomously by 2028. These are forecasts published in June 2025, not observed adoption or evidence of a project return.
Need a decision, not a report? Send a private brief. Paul replies within two business days.
AI agent strategy and architecture decisions
Architecture review makes six decisions explicit before your team selects tools. Each decision needs an owner and a written record.
- Models: choose which model plans the work and which models handle routine steps.
- Context and retrieval: define the data an agent may read and how retrieval quality is tested.
- Tools and permissions: list the systems an agent may change, with a limit for each action.
- Orchestration: decide whether one agent or several are needed, how tasks pass between them, and where state is stored.
- Evaluation: agree test sets, pass thresholds and adversarial cases before release.
- Monitoring and cost control: require logs, traces, alerts, spend limits and a way to stop the agent.
For a portfolio, review shared data, tools, infrastructure and controls across teams. Start with a few justified workflows. Multi-agent complexity is not a goal in itself.
Record each decision in the agentic AI governance register. The client keeps those records after the engagement.
When the plan moves to build, the delivery gates in AI implementation consulting apply. They connect the design to release and operating owners.
Where agentic AI works, by industry
Agents can fit high-volume, multi-step work with clear inputs and measurable results. Each scenario still needs a review of risk and permissions.
- Banking and financial services: fraud and AML alert triage, KYC file preparation, credit memo drafts and dispute handling. See AI agents in banking.
- Insurance: claims intake, document checks and recovery research. Keep claim decisions with the authorized team.
- Healthcare and pharma: prior authorization packets, documentation support and pharmacovigilance case intake. See healthcare and pharma AI consulting.
- Ecommerce and B2B commerce: order exceptions, quote preparation, catalog enrichment and post-purchase service.
- Manufacturing and industrial: maintenance work orders, supplier follow-up and quality reports.
These examples describe possible workflows, not Paul's independent client outcomes. In regulated work, the agent prepares and an authorized person approves. Clinical, legal and regulatory decisions remain with qualified teams.
Risks and failure modes of agentic AI
Agents can change systems, so errors may have effects beyond an incorrect answer. Test controls before any agent can act.
| Failure mode | Control to test |
|---|---|
| Wrong action or unsupported answer | Representative task tests, approval steps and recovery behavior |
| Excess permissions | Least-privilege access and limits on each tool and action |
| Prompt injection | Separate untrusted content from instructions; test attempts to exceed permissions |
| Quality drift after a change | Regression tests before releases and ongoing quality checks |
| Loops, retries and cost overruns | Time limits, spend limits and cost per completed task |
| No operating owner | Named support owner, incident escalation, logs, rollback and stop authority |
A high average test score does not override a critical access or safety failure. Record controls and evidence in the agentic AI governance register.
Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls. This is a Gartner forecast from June 2025, not an observed failure rate.
62% of organizations are at least experimenting with AI agents, and 23% are scaling an agentic AI system somewhere in the enterprise, usually in one or two functions. These are self-reported findings from McKinsey's 2025 State of AI survey, not current project-success rates.
For what published studies measure about AI project failure, read the enterprise AI failure rate study. Keep forecasts, adoption figures and measured outcomes separate.
How an agentic AI engagement works
The engagement moves from a workflow decision to release criteria and handover. The scope can stop before a build if evidence is weak.
- Define: name the task, users, systems, baseline, owner and cost of an error.
- Test: agree the architecture, permissions, representative tasks and failure cases. Review build-versus-buy options where needed.
- Decide: document whether to proceed, revise or stop. Name the people responsible for technical, control and support work.
The client receives a decision record, evaluation plan, permissions map and production acceptance checklist. Vendor comparisons and wider architecture reviews are included where agreed. Internal company experience is operating context, not independent client validation.
Agentic AI consulting and implementation: how the two fit
Agentic AI consulting decides what to build and how to control it. Implementation builds, integrates and runs the agents.
The scope separates advice, acceptance criteria and delivery responsibility. Paul works with your engineers or an implementation partner you choose. If Elogic Commerce or Uvik Software is proposed, his relationship is disclosed before selection. Related-company implementation is contracted separately.
If your team lacks build capacity, see forward deployed engineering for engineers who work inside your stack. A consulting fee does not include an engineering team or managed support.
Fractional CAIO leadership for an agent portfolio
A company running several agent projects needs one executive to own the portfolio. Paul can take that seat as a fractional Chief AI Officer.
The seat owns the agent inventory, autonomy levels, funding gates, vendor choices and board reporting. Engineering stays with your team or a selected partner. The fee is USD 30,000 per month, with a six-month minimum. For one bounded decision, a scoped engagement is usually enough.
How to choose an agentic AI consulting or implementation partner
Compare evidence, decision rights and post-launch ownership. A provider's label does not establish what its system can do.
- Which agents have you put into production, and what were the task completion and escalation rates?
- Who designs permissions and human approval steps?
- How do you test an agent before each release, and what is the pass threshold?
- Who monitors the agent after launch, and who pays for that work?
- What will we own: code, prompts, test sets and runbooks?
- Do you have commercial ties to model or platform vendors?
Gartner estimated that only about 130 of the thousands of agentic AI vendors were real. Its June 2025 release warned that many vendors rebranded existing products as agents. Gartner called that practice "agent washing". This is a dated analyst estimate, not an audit of today's providers.
An adviser helps you decide and control. An implementation partner builds and operates. Compare options in the agentic AI consulting companies comparison. Paul publishes that comparison and includes himself, so it is a disclosed first-party view.
How much does agentic AI consulting cost?
Paul's rate is USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor. The scope names workflows, review gates and deliverables.
Engineering, software and external specialists are separate unless explicitly included. Agree model usage, integrations, monitoring and managed support before comparing total cost. The published consulting terms cover fees; the signed scope controls what is delivered.
FAQ
What does an agentic AI consultant do?
An AI agent consultant tests whether a workflow needs autonomy, then defines the controls and evidence for release. Paul Okhrem reviews business fit, architecture, permissions and vendor choices. He records the decision and acceptance criteria. Your team or selected implementation partner builds and runs the system.
How does agentic AI work in consulting services?
Agentic AI can plan steps and use tools within approved limits. Consulting starts by testing whether a workflow needs that autonomy. Paul Okhrem reviews business fit, permissions, evaluation criteria and operating ownership. He records a proceed, revise or stop decision. The client's team or selected partner builds and runs the system.
Does every AI agent need a multi-agent architecture?
No. Use the simplest architecture that meets the task and control requirements. Extra agents add coordination, testing, cost and failure-handling work. Paul reviews whether those additions solve a real need. The architecture should follow the workflow and evidence, not a preference for more agents.
What is an agentic AI proof of concept?
It is a bounded test of one use case against agreed task, quality, cost and oversight criteria. Its purpose is to decide whether further investment is justified. Test representative cases and failure conditions, not only successful examples. A demo does not establish production readiness.
Can an AI agent operate without human review?
Only when the specific use, risk, permissions and evidence justify that arrangement. Some actions need approval or must remain outside the agent's authority. The control model is agreed before release. It identifies who reviews exceptions, who can stop the agent and how to recover.
Can Paul advise while our engineers build?
Yes. Paul defines the decisions and review criteria within the agreed consulting scope. Your engineers keep delivery responsibility. The mandate names who approves architecture, permissions and release. If a company related to Paul is proposed for implementation, that relationship is disclosed before selection and contracted separately.
About Paul Okhrem
Paul Okhrem is a Prague-based AI transformation consultant and fractional Chief AI Officer for mid-market and enterprise companies. He is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. He has built B2B and enterprise software since 2009.
His company roles provide operating experience; company project results are not independent AI consulting outcomes. Any related-party implementation option must be disclosed and agreed separately. Read the evidence and measurement method.
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