Agentic Performance Management requires three integrated components to deliver autonomous execution: transaction-level data, governance mechanisms, and intelligent agents. This architecture creates a distinct category alongside ERP and EPM in the finance tech stack.
During Nominal's recent webinar "Finance Tech Stack for 2026: Why APM is the Category You Can't Ignore," featuring Co-Founder and CTO Golan Kopichinsky and VP of Product Management Yaara Hendel, a fundamental question emerged: why does finance still require so much manual coordination despite heavy technology investment?
Finance leaders described implementing robust ERPs, close management platforms, and AI assistants, yet their teams still export data to spreadsheets for reconciliations, manually match intercompany transactions, and coordinate consolidations outside their systems entirely.
The answer isn't that existing tools fail at their intended purpose. ERPs record transactions flawlessly. Close management platforms organize workflows efficiently. The problem is architectural. These tools were designed to assist human execution, not replace it.
Agentic Performance Management emerged to solve this gap by introducing autonomous execution as a distinct layer in the finance tech stack. Watch the full webinar to see APM in action, or continue reading to understand the three essential components that must operate together to deliver autonomous finance operations.
Why APM Exists as a Distinct Category
When enterprise finance teams evaluate new software, they ask how it improves what they already have. If the answer sounds similar to existing capabilities, the conversation ends quickly. Controllers who already use close management platforms aren't looking for another tool that accelerates close.
APM creates a different conversation by addressing the execution layer that has always been missing. While ERPs serve as the system of record and EPM platforms handle planning, Agentic Performance Management autonomously executes the operational accounting work that still requires manual coordination.
This matters because enterprises can implement this new layer without replacing existing systems. The ERP continues as the authoritative source for financial data. Close management platforms keep organizing workflows. The execution layer adds autonomous capabilities that make these systems work together without constant human intervention.
Rather than competing with existing close solutions, this approach introduces a net-new layer that complements what finance teams already have.
You might also like: ERP vs EPM vs APM: Which One Actually Reduces Manual Work?
The Three-Component Architecture That Defines APM
Agentic Performance Management requires three components working together in a closed loop. Having just one or two creates assistance tools. Having all three delivers autonomous execution that fundamentally changes how finance operations function.
1. Transaction-Level Data Foundation
Autonomous execution requires examining individual transactions, understanding their context, and tracing activity across systems. Summary balances don't provide sufficient detail. When an agent needs to reconcile cash activity, it must access individual bank transactions and match them against specific general ledger entries.
The general ledger provides this transaction-level foundation. Agents pull detailed data through API connections, work at the entry level to apply accounting logic, and maintain the precision required for audit purposes. This enables agents to reason about exceptions and make contextual decisions rather than following rigid scripts.
The distinction is whether data enables autonomous execution or simply supports human analysis. When finance teams export transactions to spreadsheets for actual reconciliation work, the data exists but isn't architected for independent processing.
2. Task Management for Governance
Autonomous doesn't mean unsupervised. Finance operations require controls, audit trails, and mechanisms to handle exceptions. Task management provides the governance layer that ensures agents operate within appropriate boundaries while maintaining oversight that audit standards demand.
When agents complete workflows, they generate tasks for human review at decision points requiring judgment. When exceptions arise, the system escalates through defined approval workflows. Every action maintains documentation showing what occurred, why the agent made specific decisions, and who approved the results.
This separates APM from simple workflow automation. Project management tools route approvals. Task management in APM provides control mechanisms specifically designed for autonomous agents performing accounting work.
3. Autonomous Agent Execution
Agents own complete workflows from start to finish rather than providing suggestions. A matching agent continuously compares transactions across systems, identifies pairs that should offset, flags discrepancies, and resolves standard differences automatically while escalating unusual patterns.
These agents don't follow rigid scripts that break when data formats change. They adapt to each organization's circumstances, learn from patterns in transaction data, and apply contextual logic. An agent managing cash reconciliation learns which timing differences typically resolve themselves and which scenarios require human review.
The execution capability differs from AI assistants that suggest actions. Assistants help humans work faster. Agents perform the work themselves and escalate only when genuine judgment becomes necessary.
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Why All Three Components Must Work Together
The synergy between components creates capabilities that don't exist when any piece operates in isolation. Understanding what happens with incomplete architecture reveals why this specific combination matters.
Agents alone present obvious problems. Without governance mechanisms, there's no oversight and no audit trail. Without transaction-level data access, they can't perform the detailed work that finance operations require.
Task management alone delivers coordination tools rather than execution capability. Finance teams get organized checklists and approval routing, but someone still performs every reconciliation and prepares every journal entry manually.
The general ledger alone functions as a database. It stores transactions and produces reports accurately, but provides no mechanism to execute operational workflows autonomously.
Together, these components create a continuous closed-loop cycle. Agents pull transaction data from the general ledger throughout the period rather than waiting for month-end. They execute workflows like reconciliations and consolidations based on configured business logic. When standard scenarios occur, execution proceeds automatically. When exceptions arise, the agent generates a task for human review with complete context about what was discovered and what options exist for resolution.
Once approved, results are written back to the general ledger through proper channels, maintaining data integrity and audit trail. The GL continues capturing new transactions, which generate additional work for agents who process them continuously rather than creating period-end backlogs. This contrasts with traditional coordination models, where humans orchestrate every workflow stage. Finance teams review final outputs rather than managing individual steps.
What This Architecture Enables That Wasn't Possible Before
The three-component architecture delivers operational capabilities that assistance tools cannot provide.
Non-Linear Scaling Without Headcount Growth
Growth no longer creates proportional staffing requirements. When transaction volume increases, agents scale execution automatically. Adding subsidiaries doesn't trigger hiring plans because agents extend their work across new entities. Companies can plan for significant revenue growth while keeping expenses flat.
For a deeper dive, check out: How to Scale Finance Operations Without Adding Headcount: Data-Driven Insights from 50 Million Transactions
Compressed Close Cycles Through Continuous Processing
Close cycles compress dramatically because work happens continuously rather than concentrating at period end. Organizations implementing APM report reducing close cycles by 60 to 75 percent, resulting in time savings that translate to capacity gains.
Strategic Focus for Finance Professionals
Finance professionals focus on different work. Controllers design processes that agents execute rather than managing task completion. Analysts investigate meaningful variances rather than calculating them manually. Teams apply judgment to decisions rather than consuming capacity on repetitive execution.
Recognizing When Your Organization Needs the Execution Layer
Specific patterns indicate when existing technology leaves finance teams performing operational work manually despite having robust platforms.
Spreadsheet dependency demonstrates the gap clearly. If finance teams regularly export data to Excel for actual work rather than just presentation, recording systems aren't providing execution capability. Common signs include:
- Consolidation happening outside the financial platform
- Reconciliations performed in spreadsheets despite having enterprise systems
- Variance analysis requiring manual calculations and formatting
Period-end bottlenecks reveal capacity constraints technology should address. When close cycles extend for weeks despite comprehensive financial platforms, systems aren't eliminating execution requirements. Watch for:
- Teams working overtime during close periods
- Consultants arriving quarterly to help complete consolidations
- Extended approval cycles due to volume of manual work
Hiring plans tied to complexity growth signal execution limitations. If adding subsidiaries requires adding accountants, the organization operates within the architectural limitations of assistance tools rather than autonomous execution systems.
Building Finance Operations for Autonomous Execution
Agentic Performance Management exists as a distinct category because it solves execution rather than visibility. The three-component architecture delivers what fragmented tools cannot by creating closed-loop systems where agents perform work, governance mechanisms maintain control, and transaction-level data enables precision.
Organizations building a complete finance architecture gain operations that scale with business complexity. ERPs continue serving as systems of record. EPM platforms maintain planning functions. APM adds the autonomous execution layer that transforms stored transactions into closed books without requiring manual coordination.
This changes what finance teams accomplish. Controllers design workflows rather than manage task completion. Finance professionals apply expertise to judgment rather than repetitive execution. Operations support business growth instead of constraining it.
Want to see how autonomous execution transforms finance operations? Book a demo to learn how Nominal's Agentic Performance Management fills the execution gap through intelligent agents that handle reconciliations, consolidations, and close processes while your ERP continues as the system of record.
