Agentic AI Consulting Services

Agentic AI consulting for enterprise works backwards from the workflow, not forwards from the model. Our agentic AI implementation practice covers the whole path: suitability, build, integration, governance and the handover to your team.

What an agentic AI consulting engagement includes

Every engagement starts with a workflow, not a model. We map how the work actually moves through your organisation, identify where a decision or handoff is costing time, and establish whether an autonomous agent is the right instrument for it. Only then do we design and build.

1. Workflow discoveryWorkflow discovery and agent suitability assessment.
2. ArchitectureReference architecture and tool selection.
3. BuildAgent and multi-agent build.
4. IntegrationIntegration into the systems you already run.
5. GuardrailsGuardrails, approvals and audit logging.
6. EvaluationEvaluation harnesses and production monitoring.
7. HandoverA handover your own team can operate.

What you receive at the end of an agentic AI engagement

Each stage above ends in something your own team keeps, and the last stage is a handover they can operate. The artefacts are listed so you can hold any proposal to them, ours included. An agent is an AI application that takes action, so the same list is a fair test of any proposal for AI application development services; the wider list for AI and machine learning work is on our AI and machine learning services page.

• A suitability assessment of the workflow you brought, with a plain answer when an agent is the wrong tool and what we would do instead, and a costed roadmap before you commit to a build.
• A reference architecture and tool selection, with the integration constraints surfaced in discovery and a defined path to the environment the agent will actually live in.
• The agent or multi-agent build, integrated with the CRM, ERP, data platform and internal APIs you already run.
• Guardrails, approvals and audit logging: role-scoped permissions, human approval gates on consequential or irreversible actions, a log of what the agent did and why, and defined escalation and rollback behaviour for failure states.
• An evaluation suite of real cases that scores whole runs and runs continuously rather than once at launch, with production monitoring.
• The documentation your compliance function and, where relevant, an authorising official will ask for, and a handover your own team can operate.

When an AI agent is the wrong tool

This is the question we get asked least often and answer most bluntly.

We will tell you when that is the case. A shorter, honest engagement is better business for both of us than a pilot that quietly dies after the demo.

Why most agent pilots never reach production

Research from IDC and Lenovo found that for every 33 AI proofs of concept an organisation launched, only four reached production (CIO Playbook, reported by CIO). The reasons are rarely about the model. Pilots run on clean demo data and short paths; production brings undocumented internal APIs, legacy systems with custom fields, rate limits, permissions, and data that disagrees with itself.

We plan for that from the first week: integration constraints surfaced during discovery, evaluation criteria agreed before a line of code, and a defined path to the environment the agent will actually live in.

How agents are governed once they are live

An autonomous system that takes action inside your business needs the same controls as an employee who does.

• Role-scoped permissions.
• Human approval gates on consequential or irreversible actions.
• Full logging of what an agent did and why.
• Evaluation suites that run continuously rather than once at launch.
• Defined escalation and rollback behaviour for failure states.

For regulated and public-sector clients this extends to auditability and the documentation your compliance function and, where relevant, an authorising official will ask for. See our public sector and healthcare work.

Which workflows are worth automating first

The best first candidates are high-volume, rule-adjacent processes where a human currently reads, decides and re-keys between systems. In practice that means intake and triage, document and claims review, reconciliation and exception handling, procurement and approval routing, and reporting that someone assembles by hand every week.

If you want to sketch the numbers before you talk to anyone, our AI ROI calculator is free and ungated.

What moves the cost of an agentic AI engagement

Our rates are $150 to $250 per hour, depending on the seniority and mix of the team, and a typical project runs $50,000 to $200,000. Both figures are published on our about page. We publish no separate agentic AI price band, for the reason our AI and machine learning services page gives: a defensible one would have to come from delivered projects rather than from a market average. Where the scope is clear we quote a fixed price for a defined outcome, and discovery is short and bounded so the costed roadmap arrives before you commit to a build. These are the things that move the number.

Why work with a boutique engineering firm for AI agent consulting

Sthenos has been building and running software since 2007. That matters here for a specific reason: agentic AI is new, but integrating with a twenty-year-old ERP, negotiating a security review and supporting a system after launch is not. Those are the parts that decide whether an agent survives contact with production.

We are deliberately senior-led. The people who scope your engagement are the people who build it. We are SBA-certified EDWOSB and WOSB, which also makes us a straightforward addition to a federal or state supplier-diversity plan and a viable subcontractor on a prime's team. Rated 5.0 on Clutch across 43 verified client reviews. More on the firm on our about page.

What to ask any AI consultant before you hire one

These questions work on any agentic AI development company, including this one. Each points at a control or a decision described on this page, so an answer can be checked against it rather than taken on trust.

• Will you tell us when an agent is the wrong tool? A vendor with no such answer has never given it. Our answer is the section on when an AI agent is the wrong tool.
• How will the agent run in production, and inside which of our systems? Pilots run on clean demo data and short paths. Ask for the path to the environment the agent will actually live in before anything is built.
• Which actions sit behind a human approval, and who gives it? An approval gate stops the agent before an action takes effect. A notification after the fact is not one.
• How is AI agent security handled, tool by tool? Listen for the narrowest credential per tool, a sandbox around anything the agent executes or fetches, and permissions designed on the assumption that the documents it reads can influence it.
• What stops a run? A step ceiling, a wall-clock timeout, a spend limit per run, detection of a loop repeating itself, and a stopping condition that is not simply the model deciding it is finished.
• Can you show us, months later, why the agent did something? The log should hold the request, the retrieved context, the model and prompt versions, the tool and its arguments, the result, the approver and the identity the action ran under, in a store the agent cannot edit.
• How are whole runs evaluated once it is live? Against a fixed set of real cases, on end state, permissions, steps, spend and escalation, continuously rather than once at launch.
• Do you hold the attestations you mention, or help us prepare for them? Ask to see the report rather than accept a logo on a slide. Our own position is stated under regulation below.

The agent vocabulary, defined

Agentic AI has acquired a vocabulary faster than it has acquired shared meanings, and the words below are the ones that decide an architecture and a security review. Each definition is written so you can use it against any vendor, including this one. The wider AI and machine learning terms sit on our AI and machine learning services page.

Tool use and function calling

A tool is anything the agent can invoke that is not the model: a search, a database query, a calculation, an API in your own estate. Function calling is the mechanism, where the model returns a structured request naming the tool and its arguments and your code decides whether to run it. The important word is decides. The model proposes; your code holds the permissions, validates the arguments, applies the limits and records what happened. An agent is only ever as constrained as the layer between the model and the tool.

Planning loops

A planning loop is the cycle of deciding a next step, taking it, observing the result and deciding again. It is what separates an agent from a single model call, and it is also where cost and risk compound, because one bad step becomes the input to the next. The controls that matter are a step ceiling, a wall-clock timeout, a spend limit per run, detection of a loop repeating itself, and a defined stopping condition that is not simply the model deciding it is finished.

Memory

Memory is what an agent carries between steps and between runs. Within a run it is the working context, which is finite and has to be curated rather than allowed to grow. Across runs it is whatever you deliberately persist: past decisions, user preferences, entity state. Persistent memory is a data store with all the obligations of one, so it needs a retention policy, an access model, a deletion path and a view somebody can inspect. An agent that remembers something it should not have kept is a data incident, not a feature.

Human in the loop approval

An approval gate is a point where the agent stops and waits for a person before an action takes effect. It is not a notification after the fact. Designing it means deciding which actions are consequential or hard to reverse, who is competent to approve each class, what the approver is shown so the decision is real rather than a rubber stamp, and what happens when nobody responds. We already apply this on consequential or irreversible actions, and it is the control that decides whether an agent is allowed anywhere near production.

Sandboxing and least privilege

Least privilege means the agent holds the narrowest credential that lets it do its job, scoped per tool, rather than inheriting a broad service account because that was quicker. Sandboxing is the containment around anything it executes or fetches: a bounded environment, an allowlist for outbound calls, no ambient access to the rest of the estate. Treat the agent as an actor whose instructions can be influenced by the documents it reads, because they can, and design its permissions accordingly. Access control work sits with our cybersecurity practice.

Audit logs of agent actions

An audit log for an agent has to answer a question a normal application log does not: why. That means the request, the retrieved context, the model and prompt versions, the tool called and its arguments, the result, who approved it if anyone did, and the identity the action ran under. It has to be written where the agent cannot quietly edit it, kept as long as your obligations require, and readable by the people who will actually ask, in compliance as well as in engineering.

Evaluation of agent runs

Evaluating an agent is harder than evaluating a single answer, because the same goal can be reached by several acceptable paths and a plausible-looking run can still be wrong. What works is scoring the whole run against a fixed set of real cases: did it reach the correct end state, did it stay inside its permissions, how many steps and how much did it spend, did it stop when it should have stopped, and did it escalate the cases it was supposed to escalate. That suite runs continuously rather than once at launch, so degradation shows up before a user reports it.

The agent stack, and the categories we leave unnamed

Every product named below is named because another Sthenos page already claims it, and that page is linked beside it. Where we work in a category without standardising on one product, the category is named and no product is. A logo we have not earned would tell you less than the gap does.

Regulation, and what an agent has to prove

An agent inherits every obligation that already binds the system it acts inside. Our public sector and cybersecurity pages catalogue FISMA, NIST SP 800-53, NIST SP 800-171, FedRAMP, StateRAMP, CMMC, CJIS, Section 508, HIPAA, SOC 2, PCI DSS and GDPR, each with its issuer and whether it is mandatory, voluntary or contractual, and our government and healthcare pages name the ones that bind those sectors. An agent reading clinical notes is handling protected health information; an agent acting inside a federal system is inside that system's authorisation boundary. Neither fact changes because the project was filed under AI.

Two references are worth reading before an agent goes near a regulated process. The NIST AI Risk Management Framework was released on January 26, 2023, is intended for voluntary use, and gives you a shared vocabulary for managing AI risk and building trustworthiness into how a system is designed, developed, used and evaluated. NIST also publishes an AI RMF Playbook and states that AI RMF 1.0 is being revised as part of the White House AI Action Plan. The OWASP Top 10 for LLM and generative AI applications is the security counterpart, and its 2025 list names excessive agency as its own entry alongside prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded consumption. Read as a design checklist, it is close to a specification for the permissions, approval and cost controls described above. Deeper detail sits on our AI governance section.

The four functions the NIST framework is built from

NIST organises the framework into four functions. They are an agenda for the governance conversation rather than a score, and the wording below is NIST’s own, read on the AI RMF core section at the NIST AI Resource Center. Nothing in it is counted or ranked.

Before an agent goes live

Score yourself honestly. Anything you cannot evidence counts as a no.

• Every tool the agent can call runs under a scoped credential, not an administrator account.
• Consequential or irreversible actions sit behind a human approval, and someone is named to give it.
• There is a step ceiling, a timeout and a spend limit per run, and an alert before any of them is reached.
• Every run is logged with its context, tools, arguments, results and approvals, in a store the agent cannot edit.
• Prompts, model versions and tool definitions are versioned, so a change in behaviour traces to a change in the system.
• An evaluation suite of real cases scores whole runs, not single answers, and runs continuously.
• Failure states have a defined escalation and a rollback, and the agent can be switched off without a deployment.
• Persistent memory has a retention policy, an access model and a deletion path.
• Data that leaves your boundary has been identified, and the retention and training terms are written down.
• A person owns the outcome, and a user can dispute what the agent did.

Frequently asked questions

How is agentic AI different from generative AI?

Generative AI produces output: text, code, an image, a summary. Agentic AI takes action: it calls tools, queries systems, makes a decision and moves a process forward, usually across several steps. The engineering problem shifts from "is the output good" to "was the action correct, permitted and reversible." We cover the distinction in more depth in agentic AI vs generative AI vs traditional AI and what is an AI agent.

Will this integrate with the systems we already run?

That is the core of the work rather than an afterthought. We build against your existing CRM, ERP, data platform and internal APIs instead of asking you to replace them. Integration constraints are surfaced during discovery, because they are the most common reason an agent that worked in a demo fails in production.

What happens when an agent fails?

It is designed to fail safely. Consequential actions sit behind approval gates, every action is logged with its reasoning, failure states have defined escalation and rollback paths, and evaluation suites run continuously so degradation is caught before a user reports it. An agent with no defined failure behaviour is not finished.

How do you handle governance, risk and compliance?

Role-scoped permissions, human-in-the-loop on irreversible actions, complete audit trails, and documentation written for the people who will review it. For public-sector engagements we build to the control standards the authorising process requires. See public sector.

What does an engagement cost?

Scoped per engagement, and quoted as a fixed price for a defined outcome wherever the scope allows. Discovery is deliberately short and bounded so you get a costed roadmap before committing to a build. We would rather tell you the number early than discover it together halfway through.

Do you work with federal, state and local agencies?

Yes. We are SBA-certified EDWOSB and WOSB, registered in SAM.gov, and we work with agencies both directly and as a subcontractor on prime teams. NAICS 541511. See public sector.

Where are you based?

Headquartered in Tysons, Virginia, with a second office in North Bethesda, Maryland. We work with clients across the United States.

How do we start?

A short call about the workflow you have in mind. If an agent is the right tool we will say what it would take. If it is not, we will say that too, and what we would do instead. Talk to our team.

More about agentic AI

Ready to talk? A short call about the workflow you have in mind. If an agent is the right tool we will say what it would take, and if it is not we will say that too. Talk to our team.