Agentic & AI

Agentic AI Consulting

Most companies have already bought AI. What almost none of them have is a single process that runs end to end without a person pushing it along. That gap — between a stack of subscriptions and an operating model — is the whole job of this division. Agentic AI consulting at Facet has exactly one deliverable, and it is not a recommendation: it is a loop doing real work on your real operations, with a named operator responsible for keeping it working. We can describe that deliverable this precisely because we decomposed our own business first, one process at a time, and kept the list of the ones that came apart in our hands. Most firms in this market will assemble that list on your operation. Ours is already paid for, which is why an engagement here opens by running something rather than by assessing it. Everything else on this page is how we get there.

Why hire an agentic AI consulting partner?

Because the hard part was never choosing the technology. It was deciding which parts of your business should stop being done by a person, and then being accountable for what happens next.

Advice is not a deliverable

The standard consulting shape is a discovery phase that ends in a document, followed by an implementation phase that somebody else quotes separately. It fails for agentic work specifically, and for a reason that is structural rather than a matter of consultant quality.

• You cannot assess what has not run yet: A readiness assessment can tell you which processes are well documented. It cannot tell you which ones an agent can actually carry, because that depends on your exception rate, your data quality, and how much of the process lives in people's heads — and none of those are visible until something tries.
• The estimate is wrong in both directions: Work that looks trivial on a process map turns out to depend on a judgment call nobody wrote down. Work that looks impossible turns out to be four decisions in a trench coat. We have never seen an up-front estimate survive two weeks of contact with the actual queue.
• The document expires faster than the decision: By the time a strategy deck has been circulated and approved, the tooling has moved and the recommendation is stale. A running loop does not go stale; it gets tuned.
• Somebody has to own it on Monday: The most common failure we are called in to fix is not a bad build. It is a good build with no operator — a set of workflows that worked in month one and drifted by month four because the accountability ended when the invoice did.

Action item: Look at your last AI initiative and ask one question — is anything from it still running unattended today? If the answer is no, the problem was ownership, not the technology.

The reframe we work from

Agentic work is not an AI problem. It is a process decomposition problem wearing an AI costume. The companies getting real leverage did not find better models than their competitors; they broke their work into pieces small enough, and defined well enough, that something other than a human could hold each piece. The models are the easy part and they get better on their own. The decomposition is the part you have to earn, and it is durable — it keeps paying off across every model generation that follows.

That is the argument this division is built on, and it is why our engagements start with your work rather than with our stack.

We decomposed our own business before we decomposed anyone else's

Facet is not describing this operating model from outside it. The processes we took apart were our own: engineering, content, research, quality review and reporting all run through Agent Loops today, with a person in command of each one. That is how a boutique team carries the volume it does, and it is why the claims on this page are specific rather than aspirational.

It is also where the reframe above came from. We did not conclude that agentic work is a decomposition problem by reading about it. We found out by watching which of those processes came apart under an agent — usually in week three, when the task an agent could obviously do turned out to rest on a judgment nobody had ever written down.

That changes the commercial terms as much as the technical ones. The slow, expensive half of a conventional engagement is discovery: senior consultants reading your processes by hand and billing every hour of it. We put agents on that pass and keep our people on the conversations that need a person, which is why we can time-box a scope to two weeks and quote a fixed fee instead of opening an hourly phase with no stated end.

• Humans stay in command. Agents carry volume. A Facet engineer owns the design, the review, and every decision that reaches your customers or your systems.
• Agent work clears the same gates as human work. There is no lighter review for an output because a model produced it — not on our delivery, and not on yours.
• We name the failure modes instead of hiding them. Hallucination, scope creep, runaway token cost and lost context are all real and all predictable. Each one has a control because we hit all four on our own work first.

Our own build layer is Agentic Engineering and our own run layer is AI Managed Services — the same two things on the menu below, pointed inward before they were ever pointed at a client.

What the Agentic & AI division does

We take a process you run today, break it into pieces an agent can carry, build the loop, run it, and hand over the capability. The services below are entry points into that same arc — pick the one that matches where you are, not the one with the best name.

Decide, build, run — and why we sell all three

Most firms sell one of these three and hand you off for the other two. We do all three deliberately, because the handoffs are where the value leaks out.

• Decide: We map the processes that are actually costing you throughput, rank them by what a loop could carry, and pick one. This takes days, not a quarter, because the ranking is settled by running something rather than by workshopping it.
• Build: We decompose the chosen process, stand up the Agent Loops that carry it, and wire in the checks that stop a bad output from reaching a customer. Your standards apply to agent work exactly as they apply to human work.
• Run: We operate what we built and keep improving it, reporting against your numbers on a cadence. This is the part that compounds — and the part most engagements skip.
• Transfer: Your people learn the method while we run it, so the capability stays after we step back. We would rather be renewed because the next process is worth doing than retained because you cannot operate the first one.

Action item: Name the one process where a two-week delay costs you the most revenue. That is almost always the right first loop, and it is rarely the one that gets nominated in a planning meeting.

What the companies ahead of you actually did

There is nothing in this to be intimidated by. The businesses with real agentic leverage right now did a short list of unglamorous things earlier than everybody else, and every item on that list is available to you this quarter.

• They picked one process and finished it: Not a portfolio and not a strategy — one process taken all the way to running. Finishing the first one is what made the second obvious, and cheap.
• They started before the data was clean: Decomposition told them which fields actually mattered, and they fixed those. The companies still waiting for a documentation project to conclude are the ones who lost the year.
• They kept the compounding, and it was not automatic: The standards and the context from the first loop got written down, so the second one started from documentation instead of a blank page. That is what made it cheaper — not the passage of time. That curve is why a head start opens a bigger gap than the calendar alone would explain.
• They stopped buying advice and started owning a running thing: The distance in this market is not between companies with better models. It is between companies with something running and companies with a deck.

The useful thing about that list is that none of it depends on scale, budget, or being technical. It depends on deciding which process goes first and then not stopping halfway.

Action item: Ask what your business could quote, promise, or take on if your slowest process ran at the pace of your fastest one. That answer is a better reason to start than anything your competitors are doing.

How it works

We prove it on your work, not on a demo

The engagement starts on a real queue with real inputs, measured against a number your team already reports. We do this because it is the only version of the exercise that answers the question you are actually asking, which is not "does this technology work" but "does it work here."

• One process, fully covered: Partial automation creates a new bottleneck at the handoff and usually nets out at zero. We take one process end to end rather than three processes halfway.
• Your gates, not ours: What an agent may do unattended, what needs sign-off, and what never leaves human hands gets written down and approved before anything runs.
• Failure modes are designed for: Hallucination, scope creep, runaway cost, and lost context are known behaviors, not surprises. Each one gets a specific control, and the controls are visible to you.
• Exceptions route to a person: When a loop hits something outside its lane it stops and escalates rather than guessing. Silent failure is the only outcome we treat as unacceptable.

What one loop is worth — a worked example

Numbers below are illustrative and exist to show the shape of the math, not to predict your result. Substitute your own and the conclusion usually holds.

Take a quote-and-proposal process:

That is thirty-nine hours a week back (illustrative) — near enough a full-time role you did not have to hire. It is also the number most business cases stop at, and the less interesting one.

Here is the interesting one, and it is about slope rather than savings. Double the volume to 60 requests a week and the old process needs 90 human hours; the loop needs 12 (illustrative). Both still rise with demand — a loop with a human reviewing every request is not free of volume. What changes is the cost of each additional request: 12 minutes instead of 90. Growth used to arrive as a hiring decision; at that slope it arrives as six more hours a week. That is what moves the ceiling, and the ceiling is the thing that was actually limiting the business.

Action item: Run this table on your own bottleneck before you talk to anyone, including us. If the second row does not change much under your own assumptions, that process is not the right first loop.

Which of your processes could an agent loop actually carry? Book the Agent Loop Scope — two weeks, one real process, fixed fee, run against your live queue rather than a demo. You leave with the decomposed map and a written recommendation, including "not this process, not yet" if that is what two weeks on your own work shows.

Who should not hire this division

We would rather lose the engagement than take one that will not work. Do not hire us for agentic work if:

• You want a proof of concept for a board slide: We build things intended to stay running. If the goal is a demo by the end of the month, a demo is cheaper from somebody else.
• Nobody internally can answer questions about the process: Decomposition requires someone who knows how the work really happens, including the undocumented exceptions. Two or three hours a week of that person is the one resource we cannot substitute for.
• The process is genuinely low volume: If something runs four times a month and takes twenty minutes, automating it is a hobby. Fix the thing that runs four hundred times.
• You need the humans gone by a fixed date: Agentic work moves the ceiling well before it removes roles, and engagements framed as headcount reduction tend to lose the internal cooperation they depend on.

Agentic AI consulting FAQ

What is agentic AI consulting? It is the practice of identifying which parts of your operation can be carried by AI agents, decomposing those processes so agents can hold them reliably, and building and running the result under controls you approve.

• The unit of work is a process, not a tool: The output is a specific job getting done differently, not a technology installed and handed over.
• It is build-inclusive by nature: The assessment and the build are not separable, because the only reliable assessment of whether a process can be agenticized is a loop attempting it.
• It assumes human ownership: Agents carry volume; your people own judgment, escalation, and every decision to ship. Accountability does not move.
• It is measured on your existing numbers: Cycle time, throughput per person, error and rework rates — the same measures you would apply to any operational change.
• It compounds where projects do not: Each cycle informs the next, so a well-run loop is worth more in month six than in month one, provided someone is operating it.

How is this different from buying AI tools for the team? A tool makes an individual faster at a task they already own. A loop takes the task. That difference decides whether the gain shows up in your operating numbers or evaporates into people's calendars.

• Tools distribute gains, loops concentrate them: Twenty people saving twenty minutes a day is real and almost impossible to see in a business metric. One process running at five times the throughput is visible immediately.
• Tools need an owner; loops come with one: A subscription does not monitor itself or answer for itself. Managed operation supplies the team that does.
• Tools plateau; loops are tuned: Adoption peaks and settles. A loop under active operation keeps improving because someone is responsible for improving it.
• Governance differs completely: Individual tool use is hard to audit. A loop has defined boundaries, logged actions, and a record of what was proposed, what passed, and what a human caught.

How long before we see anything real? The first loop runs against live work inside weeks, not quarters, because we scope to one process and refuse to widen until it holds.

• Week one is decomposition: We map the actual process, including the exceptions nobody documented, and agree the boundaries.
• Week two puts something in front of real inputs: Shadow mode first, where the loop runs alongside your people and its output is compared rather than shipped.
• Then it takes work off the queue: Once the comparison holds, the loop starts carrying volume with human review on the way out.
• Widening comes after evidence: We expand coverage when the numbers earn it, not on a predetermined ramp.

Who owns what we build? You do — the running system, the configuration, and the method behind it. Our model is capability transfer rather than dependency.

• It lives in your environment: Your repositories, your accounts, your data. Nothing essential is hosted somewhere you cannot reach.
• The method is documented, not held: Boundaries, controls, and operating procedures are written down and handed over as part of delivery.
• Your people learn it while we run it: We work inside your team rather than beside it, specifically so the knowledge does not leave when we do.
• Leaving is not punished: If you take operation in-house, you start from a working system rather than a blank page. That is the intended outcome, not a failure of the engagement.

Do we need clean data or documented processes first? No, and waiting for either is the most common way companies lose a year. Decomposition surfaces the gaps far more efficiently than a documentation project does.

• The loop finds the gaps for you: Where an agent stalls is precisely where your process is underdefined. That is diagnostic information you could not have bought any other way.
• Data quality gets fixed where it matters: You clean the fields the loop depends on, not the whole warehouse, which is a project of a completely different size.
• Documentation falls out of the work: The decomposition is the documentation, and unlike a process-mapping exercise it is kept current because something executes against it.

Start With One Process

If you are skeptical of AI consulting, you should be. The category has produced more decks than running systems, and most of the value promised in 2024 is still sitting in a shared drive somewhere. Our answer is not a better deck — it is to be measured on whether something is still running unattended in your business six months from now.

Facet has been leading companies through technology transformation since 2014 and was named a CIOReview Top 10 Digital Transformation Services Company in 2021. Pricing is a fixed fee, quoted in full before work starts. We do not bill by the hour, because hourly billing pays us to be slow at exactly the work we are claiming to accelerate.

Book the Agent Loop Scope: two weeks, one real process, fixed fee. You get a decomposed process map including the exceptions nobody documented, one loop running against your live queue in shadow mode, and a before-and-after on a number your team already reports. At the end you decide whether to run it, and you keep the map either way.

And if those two weeks show the process is not worth a loop — too little volume, too much judgment per case, or data that is not there yet — the recommendation will say so in writing, with the reasoning. We would rather write that sentence than sell you a build that quietly stops running by month four, which is the outcome we get called in to fix more often than any other.