How AI Agents Should Reason in Enterprise Back-Office Use Cases

Very few conversations about enterprise AI go into detail about how AI agents should actually make decisions for back-office processes like reconciling invoices, closing your books, or matching supplier documents across four different systems. Even fewer off-the-shelf AI platforms have the capability to use different reasoning approaches for different situations.

That matters because different back-office problems require fundamentally different reasoning strategies. An agent that monitors your ERP and HCM applications for anomalies needs to think differently than one that matches an invoice to a purchase order, a goods receipt, and a contract. Getting this wrong is why so many enterprise AI deployments underperform.

Vendors apply one reasoning approach to every problem, ship a generic copilot, and wonder why 95% of enterprise AI pilots deliver zero measurable return (MIT, 2025). Microsoft 365 Copilot is already bundled into 450 million commercial seats, yet only 3.3% of users pay for it. The tool is right there, and people still don't use it. The problem was never access. It was value.

We build agentic AI for Oracle, Workday, and SAP back-office automation and any other system they interact with across your corporate ecosystem. Here are the four reasoning strategies we use in production and how each one maps to the work our agents actually do.

What follows is the reasoning architecture behind our production agents. Four strategies, each designed for a specific class of enterprise problem. The differences aren't academic - they determine whether your agent handles exceptions gracefully or falls apart the moment real data deviates from the demo script.

ReAct: Think, Act, Observe, Adapt

Where we use this: Month-End Close Automation

Month-end close is not a single task. It's a chain of dependent steps where the output of one decision shapes what the agent does next. The agent identifies unposted journal entries in Oracle, reasons about why they're stuck (missing approval? failed integration payload? data format error?), takes a corrective action (resubmits the payload, flags the approver, reformats the data), then observes whether the issue resolved before moving to the next item.

ReAct is the right fit here because the agent can't pre-plan a close sequence. It doesn't know what it will find until it looks. A failed integration between your cloud ERP and a downstream system might require a data correction, a retry, or an escalation to a human reviewer - and the agent only knows which path to take after it inspects the error. Each observation feeds the next reasoning step.

In practice, our close agents process dozens of exception categories this way: invoice accrual mismatches, missing exchange rates, payment failures, period close blocks. The ReAct loop means the agent handles each exception on its own terms rather than following a rigid script that breaks the moment something unexpected appears.

Simple Feedback: Generate, Evaluate, Retry

Where we use this: Journal Entry Automation

When an agent creates a journal entry, the validation criteria are explicit and deterministic. Does the account combination exist in the chart of accounts? Do segment values match the COA Validation Rules? Do debits equal credits? Is the period open? Is the amount within approval thresholds?

Simple Feedback handles this cleanly. The agent drafts the journal entry, the evaluator checks it against the validation rules, and if something fails - say, an invalid intercompany segment or an out-of-balance condition - the agent gets specific feedback on what's wrong and regenerates the entry with the correction applied. No philosophical reasoning required. Just: "This segment doesn't exist. Fix it and resubmit."

This direct loop is what makes journal entry automation reliable at scale. The agent isn't overthinking. It's generating, validating, and correcting in tight cycles. And because every validation step and correction is logged, the full generate-evaluate-retry chain becomes part of the audit trail. When an auditor asks "why did the system create this entry?", the feedback log answers the question without anyone reconstructing the logic after the fact.

We also apply Simple Feedback in our AI monitoring product. When the agent detects something unusual in your ERP processes - a spike in invoice uploads, a deviation in depreciation calculations, a pattern that doesn't match historical norms - it generates an alert assessment, evaluates it against configured thresholds and known events (period close, system migration, seasonal patterns), and only if the assessment passes the evaluator's criteria does it fire the alert. If the initial assessment doesn't clear the bar, the agent adjusts and re-evaluates. This eliminates the flood of false positives that makes most monitoring tools useless within a month.

Reflection: Self-Evaluate, Learn, Improve

Where we use this: Rule-Based Supplier Invoice Matching (4-Way)

Matching an invoice to a purchase order sounds simple until you've done it across procurement, receiving, and finance in systems that don't share a common data model. A 4-way match validates the invoice against the purchase order, the goods receipt, the contract terms, and potentially an inspection or acceptance step - across multiple platforms.

The hard part isn't the match. It's the mismatch. An invoice arrives for $47,200 against a PO for $44,000. Is the difference a valid additional line item? A price escalation clause in the contract? A duplicate charge? A currency rounding issue across 340 items?

Reflection is essential here because the agent needs to learn from each matching cycle. When it initially flags an invoice as a duplicate but a human reviewer overrides it because the additional line item was covered under a contract amendment, the agent doesn't just accept the correction - it reflects on why its reasoning was wrong. Was it missing context about contract amendments? Was it weighting the amount discrepancy too heavily relative to the line-item analysis? Those reflective insights get stored and applied to the next similar scenario.

Over time, the agent builds a genuine understanding of each supplier's patterns. It learns that Supplier A routinely includes expedited shipping as a separate line item that exceeds the original PO amount. It learns that currency rounding variances under $500 across large item counts are almost always immaterial. It learns which types of discrepancies are real problems and which are normal business. This is fundamentally different from a rules engine that would just flag anything outside a 5% tolerance and dump it into an exception queue with no context.

In enterprise workflows, reflection refines classification and routing rather than core accounting logic. Corrections improve how exceptions are interpreted without changing underlying rules.

ReWOO: Plan Everything, Then Execute

Where we use this: External Reconciliation

External reconciliation is one of the hardest problems in enterprise finance because the agent is comparing documents from outside the organization against internal records - and the external documents come in every format, language, naming convention, and template imaginable.

We built a reconciliation agent for a global enterprise that receives over 4,000 documents annually from 250+ external counterparties across 30+ countries. Each counterparty sends their version of the same standard document types, but in their own templates and formats. One country sends clean Excel files. Another sends scanned PDFs with handwritten amendments. A third uses naming conventions that bear no resemblance to the internal system's records.

ReWOO is the right strategy here because reconciliation is fundamentally a structured, repeatable workflow. The Planner already knows the steps: extract data from the external document, normalize it, pull the corresponding internal records, match line items, calculate variances, and generate a summary. These steps don't change between submissions - what changes is the content.

This aligns with how finance teams already work: close calendars, reconciliation schedules, and reporting cycles are planned upfront. ReWOO succeeds because it mirrors an existing operational structure rather than introducing a new one.

The Planner maps out the full extraction and matching plan for each document, including which extraction approach to use based on the counterparty's known profile (template-based for clean Excel files, semantic extraction for unrecognized formats, OCR with handwriting recognition for scanned documents). Workers then execute all extractions and data pulls in parallel - grabbing the external document data, the internal accrual records, the counterparty's historical profile, and any relevant currency or rate tables simultaneously. No waiting between steps. No LLM reasoning after each tool call.

The Solver then takes all that gathered evidence and produces the reconciliation output: matched items, variances with root-cause explanations (naming mismatch, rate difference, volume dispute, missing entries due to data lag), and recommended actions. Country profiles - AI-managed validation rules tailored to each counterparty's known template format, naming conventions, and historical submission patterns - make the Planner smarter over time. The agent learns which extraction strategies typically succeed for each counterparty, so the plans become tighter and execution confidence increases with every cycle.

First-submission acceptance rates in production went from roughly 60% to 90%, eliminating weeks of back-and-forth that previously extended reconciliation timelines. And because ReWOO batches all the tool execution into a single phase, the agent processes each document with roughly 5x fewer tokens than a ReAct approach would require - which matters when you're processing 4,000+ documents a year.

Why This Matters for Enterprise 

The gap between a chatbot that answers questions about your data and an agent that actually does the work lives in these reasoning strategies. Most enterprise AI products use a single prompting approach for everything and hope for the best. That works for answering a question about last quarter's revenue. It doesn't work for closing your books, matching invoices across four systems, or reconciling documents from 250 counterparties in 30 countries.

In practice, enterprise agents rarely rely on a single reasoning strategy. 

ReAct for dynamic, unpredictable exception handling. Simple Feedback for fast validation loops with clear criteria. Reflection for learning from corrections and building institutional knowledge. ReWOO for structured, high-volume workflows where efficiency matters as much as accuracy.

Selecting the right reasoning strategy for each use case - and combining strategies within a single agent when the problem demands it - is what separates AI that demos well from AI that survives month-end. The architectural challenge is therefore not selecting one reasoning pattern, but enabling safe transitions between reasoning modes while maintaining auditability and control

Every agent we deploy uses the reasoning approach that fits the problem, not the one that's easiest to implement. And every reasoning step is logged, traceable, and auditable, because in enterprise finance, an answer you can't explain is an answer you can't use.