There’s a version of the AI story that makes everything sound easier than it is.
Pick a model. Connect some tools. Wrap a workflow around it. Add approval steps. Call it an agent.
In reality, building agents for real operational work is much less about model cleverness and much more about system design. The hard part is not getting an LLM to sound intelligent. The hard part is getting a system to operate reliably inside messy, high-stakes environments where data is fragmented, ownership is blurry, edge cases are constant, and mistakes are expensive.
The more we build, the more one belief keeps getting reinforced:
If you want to build an agentic layer for Finance Operations, reconciliation is not just a good starting point. It is the necessary foundation.
This is not because reconciliation is the biggest workflow in finance, or the most strategic one, or the most visible one.
It is because reconciliation sits at the exact intersection of the things agents need in order to become useful: structured feedback loops, a definable source of truth, observable outcomes, clear exception handling, and a natural path from human review to progressive autonomy.
Said differently: before an agent can help run finance, it has to prove that it can reconcile reality.
The corporate mirror effect
One thing we’ve learned building with AI is that agents act like a mirror.
They reveal how the organization actually works.
If your internal knowledge is fragmented, the agent feels that fragmentation immediately. If nobody is fully sure which report is canonical, the agent runs into the same confusion a new hire would. If exceptions are handled through tribal knowledge instead of explicit rules, the agent inherits the ambiguity. If there is no clear owner for a workflow, there is nobody to define whether the agent was “right.”
This is why so many AI projects feel impressive in demos and shaky in production. The intelligence is often there. The operating system underneath is not.
That pattern is showing up broadly across enterprise adoption. PwC’s 2025 AI Agent Survey found that 79% of executives say AI agents are already being adopted in their companies, and 88% expect AI-related budgets to rise because of agentic AI. But only 34% say agents are being used in accounting and finance, which is telling: interest is high, but the move into more controlled, high-consequence environments is slower.
That gap makes sense.
Finance is not a domain where “mostly right” is enough. It is a domain where trust is built on evidence, controls, consistency, and auditability.
So when companies ask how to accelerate toward AI in FinOps, our view is that the first question should not be “where can an agent save time?”
It should be: where can we build trust, feedback, and operational truth first?
For us, that points very clearly to reconciliation.
Why reconciliation matters more than it gets credit for
Reconciliation is often treated as back-office plumbing. Necessary, but not strategic.
We think that understates what it really is.
At a systems level, reconciliation is the process through which a company tests whether its version of reality actually holds across systems. Did money move the way we think it did? Does the processor agree with the ledger? Does the bank agree with the ERP? Is the difference timing, mapping, logic, or an actual operational break?
That makes reconciliation unique. It is not just a reporting exercise. It is one of the few places in the business where operational truth gets forced into the open.
And that matters for AI.
Agents do best when they operate in environments with clear feedback loops. Reconciliation naturally creates those loops. A match is either supported or it is not. A discrepancy can be explained or it cannot. A proposed resolution is either aligned with the evidence or it is not. Over time, that creates the kind of grounded learning environment that more open-ended finance workflows often lack.
This is also where “source of truth” stops being a slogan and becomes an operational requirement.
An agent cannot work well in finance if it is constantly guessing which dataset is authoritative, which rule is current, or which person should break a tie. Reconciliation forces that clarity. It makes teams define canonical sources, document logic, and expose where decisions are still living in people’s heads.
In that sense, reconciliation is not just a workflow to automate. It is a mechanism for making the rest of Finance Operations more legible to machines and humans alike.
The best way to onboard an agent is the same way you onboard a person
Another reason reconciliation is such a strong foundation is that it lends itself to a safer model of autonomy.
We increasingly think about agents the same way strong teams think about people: not as magical systems that should immediately take action, but as teammates that earn trust in stages.
No serious operator gives a new employee full system access on day one. First, you check whether they understand the problem. Then whether they can propose the right response. Then whether they can execute in a limited way. Only after that do you expand scope.
We think agents should be introduced the same way.
Reconciliation is ideal for this kind of progressive autonomy because the workflow breaks down naturally into layers:
- understanding the records and context
- identifying the likely root cause of a mismatch
- proposing a resolution
- and only later, taking action across connected systems
That progression is much harder to achieve in fuzzier workflows where the boundaries of “good judgment” are less explicit.
With reconciliation, you can stage autonomy in a way that is observable. First the agent explains. Then it recommends. Then it acts, under control.
That is a much better recipe for trust than asking a finance team to hand over execution from the start.
Why black-box AI breaks down fastest in finance
A deeper issue here is accountability.
A person can be responsible for a decision. An AI system cannot, at least not in any meaningful organizational sense.
If an agent makes a critical mistake, the burden does not fall on the model. It falls on the team that designed the workflow, approved the access, and trusted the output.
That is why we do not think “black box AI” is a serious operating model for finance. If an agent is going to participate in core financial workflows, it has to be observable. You need to understand what it saw, how it reasoned, which tools it called, what decision it made, how long it took, and where uncertainty entered the process.
Reconciliation is a uniquely strong environment for this because it already revolves around evidence trails. Records can be traced. Matches and mismatches can be justified. Adjustments can be reviewed. Exceptions can be audited.
That does not make the problem easy. But it does make it tractable.
And tractable is what matters when you are trying to build real autonomy.
The strongest parallel we see is in software engineering
If this thesis sounds too specific to finance, there is another industry where a very similar pattern is already becoming obvious: software engineering.
Coding is one of the most promising use cases for AI agents, and not by accident. Coding agents work especially well because code can be verified through automated tests, agents can iterate using test feedback, the problem space is relatively well structured, and output quality can be measured objectively.
That point is bigger than coding.
What made software such fertile ground for agents was not just that models got good at writing code. It was that software teams had already built the surrounding control layer: version control, pull requests, test suites, CI/CD pipelines, code review, staging environments, and observability.
In other words, the agent did not arrive into chaos. It arrived into a system designed to verify, constrain, and improve its work.
There is also a useful cautionary datapoint here. Stack Overflow’s 2025 Developer Survey found that 84% of respondents are using or planning to use AI tools in development, and 51% of professional developers use them daily. But trust is much more limited: only 29% said they trust AI outputs to be accurate, while 46% said they distrust them.
That feels very familiar to what is happening in finance.
People want the leverage. They do not yet trust unconstrained autonomy.
In software, tests and CI became the control layer that made coding agents useful. In Finance Operations, reconciliation can play that role.
It is the environment where agents can be grounded, evaluated, constrained, and improved before they are trusted with broader workflows.
Reconciliation creates the right hybrid architecture
One of the common mistakes in early AI projects is using LLMs for tasks that should remain deterministic. Not everything benefits from probabilistic reasoning. Sometimes Python should just stay Python.
The most effective architectures we have seen are hybrid. Use AI for interpretation, pattern recognition, explanation, and navigating messy inputs. Use deterministic systems for validation, calculations, rule enforcement, permissions, and transaction-safe execution.
Reconciliation naturally pushes you toward that architecture.
It makes it obvious where AI adds leverage and where deterministic logic should remain in control. You can use models to interpret file formats, cluster exceptions, suggest likely causes, and draft recommendations. But the actual controls around matching logic, thresholds, posting rules, approvals, and system actions should be explicit and verifiable.
That hybrid boundary is not a limitation. It is the architecture that makes agents usable in finance.
So what does this mean in practice?
Our belief is that companies serious about AI in FinOps should start smaller and more structurally than they think.
Not with “replace the finance team.”
Not with “autonomously close the books.”
Not even with the broadest workflow that seems most exciting.
Start where reality can be tested.
Start with a reconciliation workflow that is frequent, painful, and operationally important. Use it to define ownership. Clarify source systems. Make exception logic explicit. Build observability. Introduce the agent first as an analyst, then as a recommender, and only later as an actor. Expand autonomy as trust is earned, not assumed.
That path may look less flashy than the top-down AI transformation narrative.
But it is more likely to work.
Because the future of agentic FinOps will not be built on model demos alone. It will be built on operating environments where truth can be verified, behavior can be observed, and autonomy can grow without breaking trust.