Agentic Performance Management is the category Nominal created for accounting teams that need more than automation. This hub brings together the definitions, use cases, comparisons, and resources that show what APM looks like in practice, starting with a live walkthrough below.
The clearest way to understand Agentic Performance Management is to see it in action. This session covers what it is, what it isn't, and where agents take on reconciliation, consolidation, and close work end to end.
For a complete view of the category, the full playbook covers the four core principles, seven use cases with customer results, a governance framework, and an evaluation checklist for separating true APM from tools that only look the part.
Finance teams have spent years layering point solutions on top of an ERP: close management tools that coordinate people, consolidation software that reports after the fact, and copilots that answer questions without acting on them. None of these execute the underlying accounting work. Agentic Performance Management closes that gap.
APM describes a coordinated system of specialized agents that operates on top of an existing ERP to independently complete multi-step accounting workflows, with traceability and human oversight built in.
Rather than assisting a person through reconciliation, consolidation, or flux analysis, the agents perform the work directly and escalate only genuine exceptions for review. The result is a system that acts on the books rather than one that only describes them.
This distinction matters because the categories APM gets compared to (ERP, EPM, RPA, and AI copilots) all play a different role in the accounting stack. Each is covered in more depth further down this page.
Related reading:
Four principles define what separates Agentic Performance from every adjacent category of tool.
Execution, not suggestions
Agents complete accounting workflows rather than recommend a next step for someone else to take. Controllers and finance leaders shift from doing the work to reviewing it, reserving judgment for the decisions that actually need it.
A continuous close
APM treats "done" as a daily state rather than a month-end milestone. Agents validate, reconcile, and keep the books ready for close in near real time, which cuts down the scramble that typically defines the final days of a period.
ERP-agnostic, multi-entity by design
The platform works across multiple ERPs, entities, and currencies without a system migration, functioning as a control layer that unifies fragmented structures rather than replacing them.
Human-in-the-loop governance
Every action runs inside rules, thresholds, and approval paths defined in natural language. Teams decide when escalation is required and can access full documentation at any time.
Related reading:
Accounting teams adopt this approach to solve specific, recurring workflow problems rather than to install a general capability. The seven use cases below show where the agents are already doing the work.
Bank reconciliation is still largely manual for companies running multiple accounts, and errors often go undetected until close or an audit surfaces them.
This turns reconciliation into a continuous workflow, matching cash at scale, surfacing exceptions in real time, and routing approved entries back to the general ledger with full traceability, as covered in Nominal's approach to continuous bank reconciliation.
In a traditional close, "done" is a moving target reached only after days of concentrated work, followed by weeks of stale numbers until the next cycle.
This model matches transactions, reconciles accounts, runs flux analysis, and flags policy exceptions continuously, so only true issues reach a human reviewer, a shift explored further in Continuous Close.
Everyday events like invoices, vendor bills, and cash receipts still require manual follow-up: reversing accruals, updating intercompany entries, running variance analysis.
Trigger agents listen for these events as they hit the ledger and respond with a recommended action the moment they occur, routing everything for review and approval, a process explained in Trigger-Based GL Automation.
Complex intercompany reconciliations can span hundreds of entities and multiple ERPs, and manual matching across teams and time zones creates delays that compound as entity count grows.
Agents reconcile high-volume intercompany activity automatically, using instructions expressed in natural language, and flag discrepancies with the full context needed to resolve them quickly, a process broken down in Intercompany and Multi-Entity Reconciliation.
Flux analysis is often high-level and retrospective because teams cannot feasibly review every account, every period, at the transaction level.
A dedicated flux agent runs full-population review across every account and entity, tracing each variance back to the transactions that caused it and routing genuine exceptions for sign-off, the approach behind Full-Population Flux Analysis.
Internal audit is frequently under-resourced, so issues surface late and audit prep turns into a scramble through spreadsheets and email threads.
Transaction patrol and policy agents monitor the ledger continuously, and every action and approval carries a complete, traceable audit trail that teams can drill into from any balance in a few clicks; the same discipline covered in Financial Audit With AI.
Revenue leakage from missed, misclassified, or split transactions often hides in plain sight, since manual reviews typically rely on sampling rather than full coverage.
Agents continuously scan every transaction for missing entries, misclassifications, duplicate invoices, and policy violations, providing context and a recommended action whenever an exception surfaces, a workflow covered in more depth in Transaction Patrol and Revenue Protection.
Agentic Performance Management gets compared to categories it isn't, from the ERP that records transactions and the EPM platform that plans against that data, to RPA scripts that follow fixed rules without judgment and AI copilots that draft answers for a person to act on.
This approach operates on top of all of these, executing the accounting work that would otherwise require direct human involvement, with logics and learning mechanisms that adapt to how an entity's books actually behave.
APM vs. ERP and EPM
An ERP serves as the system of record, and an EPM platform plans against that record once it has been cleaned up. Agentic Performance Management sits between the two, executing the reconciliation and consolidation work that determines whether the data reaching EPM can actually be trusted, a distinction unpacked in APM vs. ERP vs. EPM.
APM vs. BPM and EPM
Business process management tools coordinate workflow across a team, and enterprise performance management tools plan and forecast against whatever numbers they're handed, but neither one executes the underlying accounting work the way these agents do, a gap laid out in BPM vs. EPM vs. APM.
APM vs. Close Management Tools and EPM
Close management software digitizes checklists and tracks who owns each task, yet a person still has to complete the work behind every line item on that list. This platform replaces the coordination layer with execution, since agents run the matching, reconciliation, and flux work the checklist was only ever tracking, the full comparison covered in APM vs. Close Management.
Telling Them Apart in a Demo
The clearest way to separate true APM from a tool that only sounds agentic is to ask a few direct questions during evaluation:
• Does the tool complete workflows end to end, or does it only produce suggestions for someone else to act on?
• Can it write back to the general ledger, or does it stop at a recommendation?
• Does it run continuously, or only at close?
• Are approval thresholds and escalation rules explicit, or is governance unclear?
• Is there a full audit trail with transaction-level support behind every number?
• Does it work across existing ERPs without requiring a migration?
A platform that can't answer all six clearly is likely a rules engine or a coordination layer wearing agentic language, not Agentic Performance Management.
Execution alone doesn't guarantee control, which is why every agent operates inside a defined set of rules: discrepancy thresholds, approval workflows, and escalation paths configured in natural language rather than custom code.
Every action carries documentation and a complete audit trail, and the underlying architecture is designed to support SOC 1 controls. Human reviewers can approve, reject, or refine any result, keeping accountability with the team even as agents take on the execution.
Multi-entity accounting teams already running Agentic Performance Management report similar results: faster closes, fewer manual hours, and cleaner audit trails. The examples below span different industries and company sizes, each illustrating a different piece of what it can execute.
Green Street Power Partners is a national solar energy developer managing multiple holding companies, funds, and more than 280 project entities. Before Nominal, the finance team spent days each month untangling activity across that structure in Excel.
With intercompany eliminations and multi-entity consolidation now running without manual intervention, the team saves more than 60 hours a month, consolidates all 280+ entities with a click, and tracks 115+ leases under ASC 842 with real-time visibility into the business.
Kunai, a multi-entity IT engineering consultancy operating as part of the PwC network and preparing for acquisition, needed buyer-ready financials without a months-long ERP overhaul. Nominal layered onto the company's existing QuickBooks and Excel setup, mapped a 600-line profit and loss statement into an 18-line structure, and handled currency consolidation and intercompany eliminations without manual intervention.
The result: 80+ hours saved during due diligence, $20K avoided in staffing costs, and one full day cut from the monthly close. Head of Finance Greg Hood credited the shift with cutting Kunai's time to acquisition and avoiding an ERP implementation altogether.
Leanpay, a Buy Now, Pay Later provider operating in five countries, had been closing and consolidating in Excel across entities and currencies with limited visibility into intercompany eliminations. After connecting to Leanpay's ERP, Nominal built a daily data feed with mapping, consolidation, and multi-currency translation handled without manual intervention. The result: a 25% faster month-end close, 2+ days cut from every close, and all five countries consolidated in one system.
Team Car Care is the largest Jiffy Lube franchisee in the United States, running nearly 500 locations across six entities. Its point-of-sale system and ERP don't communicate bidirectionally, so every mismatched shipment quantity became a manual trace across hundreds of locations, a job that had absorbed four full-time people before Nominal got involved.
Within months of going live, agents were handling 70% of inventory reconciliation automatically, freeing the person who used to run that process to move into GL leadership while someone else supervises the agent workflow instead.
Agentic Performance Management works best seen against real entities and real close cycles. A short session with the Nominal team shows what agents would handle inside your current structure.
