1. The Pain and the Promise

For finance teams, bank reconciliations have long been a high-effort, low-reward ritual. Thousands of transactions, multiple data sources, inconsistent dates, and timing differences - all leading to endless spreadsheets and late nights. Yet, the reconciliation process sits at the heart of financial integrity.

What if this process could become autonomous, reliable, and transparent? That’s the promise of agentic AI - not just to automate, but to understand and act with context.

2. The Foundation: From Data to Context to Workflows

In our earlier pieces, we outlined the journey toward truly intelligent automation:

• Data is the Foundation: Without trusted, unified data, agents are just guessing. Bank feeds, ERP ledgers, and journal files must speak the same language.

• Context is King: Data alone isn’t enough. Agents need to understand what each transaction represents: a customer refund, a payroll batch, or an internal treasury transfer.

• When Context Meets Complexity: The Workflow Problem: The final step is orchestration - knowing how to resolve exceptions, propose actions, and complete the workflow with traceability.

Each of these pillars converges inside Stacks’ Bank Rec Agent, the first in a growing family of intelligent agents designed for finance teams.

3. Inside the Bank Rec Agent

The Bank Rec Agent is designed to manage end-to-end reconciliation — from ingestion to resolution. The experience begins at the account level:

Figure 1: Account-level view showing reconciliation progress and outstanding issues. The agent has cleared 80% of transactions, leaving exceptions, proposed matches, missing entries, and any unmatched transactions.

At a glance, controllers can see:

• GL balance vs. bank balance

• Amount remaining to reconcile (in this case, €12,400.41)

• Matching progress, broken down by agent vs. manual work

Below, every exception is clearly categorized:

• Proposed match: A suggested match between ERP and bank data, flagged for human confirmation due to inconsistent dates or metadata.

• Anomaly entry: A deviation from typical behavior (e.g., rent higher than expected).

• Missing entry: An unmatched transaction requiring a journal entry.

4. Learning from Every Reconciliation

What sets this agent apart is how it learns. Rather than applying static rules, it continuously refines its understanding of each account and entity.

Figure 2: The Bank Rec Agent’s learnings - showing how it remembers patterns like date lags, batch identifiers, or vendor-specific journal rules. This memory makes future reconciliations faster and more accurate.

For instance, in the HSBC EUR account above, the agent has learned that:

• Internal treasury transfers reconcile based on amount and date, even without shared references.

• Batch payments identified by ERP references typically settle one to four days after posting.

• Vendor-specific entries (e.g., Ottolenghi catering) are best handled through journal entries.

These learnings aren’t just annotations. They’re dynamic rules shaping how the agent proposes matches, flags anomalies, and automates journal entries in the next close.

5. Why This Matters

This is where data, context, and workflow truly meet:

• Data: Unified ingestion from banks, ERPs, and files.

• Context: Domain intelligence that understands what each transaction means.

• Workflow: Orchestration that knows when to automate, when to escalate, and how to document the audit trail.

The result? Finance teams move from reconciling transactions to supervising intelligence. Instead of hunting mismatches, they’re validating recommendations.

6. The Impact

Early adopters of the Bank Rec Agent have seen:

• Up to 90% reduction in manual matching.

• 50% faster month-end close.

• Dramatically improved audit readiness, with a full trail of how each transaction was resolved.

But perhaps the most important impact is trust. Controllers no longer wonder if automation skipped a step, they can see every decision, every rationale, and every learning.

7. What’s Next

The Bank Rec Agent - Agent 1 - marks the beginning of a new chapter for finance automation. Over the coming months, Stacks will introduce additional agents that extend this intelligence across the close: from journal automation and intercompany reconciliation to variance analysis and cash flow intelligence.

Each agent will share the same foundation: clean data, contextual understanding, and workflow orchestration ensuring that finance teams can trust automation at every step.

The era of manual bank reconciliation is ending. The era of intelligent, autonomous finance has begun.

Want to see it in action? Fill in the form below and we will reach out to schedule a demo.