Agentic AI in Corporate Treasury

Understanding Agentic AI

AI (including LLMs) is usually reactive: you ask → it answers. Agentic AI is AI that can act autonomously, based on objectives, not just prompts.

This means: it receives a mission (“do the daily reconciliation”) and knows how to search for data, apply steps, and self-correct if it makes mistakes.

The difference:

• An assistant who answers your questions, versus
• A good junior colleague who knows how to execute tasks independently after you’ve explained once

How Agentic AI Works (Simplified)

1. Objective – You tell it what you want (“I want the daily cash report”)
2. Planning – It creates its own list of steps (e.g., open statements, make summary, send by email)
3. Action – It runs the steps in systems (via API/RPA)
4. Feedback loop – It checks the results and adjusts itself

Examples for Treasury

Cash Reporting Agent

Collects bank statements, loads them into Excel/Power BI, calculates the daily position, and sends the report at 9:00 AM.

FX Risk Monitoring Agent

Checks exchange rates daily, compares them with the company’s exposures, and sends an alert if risk exceeds a threshold.

Payments Control Agent

Scans the payment list before execution and raises alerts for unusual transactions.

Forecast Update Agent

Updates the forecast when new accounting data appears, without requiring manual input.

Available Tools for Agentic AI

1. LangChain + Llama/Mistral (open-source) → runs locally, no costs
2. Microsoft Copilot Studio → create agents for Teams/Outlook, integrable with internal data
3. Zapier + AI Actions → cross-system automations with intelligent steps
4. Power Automate + AI Builder → simple agents for document reading, payment classification, alerting

So, agentic AI is not “magic”—it’s AI that can take initiative on repetitive processes. The difference from classic AI: you don’t wait to write a prompt, it knows what it needs to do daily. In treasury, the first areas are: reporting, forecasting, payment controls, and reconciliation.

Key question when evaluating solutions: “What can the agent do autonomously, what requires manual input, and how do I verify its decisions?”

The Difference Between Agentic AI and RPA

RPA (Robotic Process Automation)

• Does intelligent copy-paste: moves data between systems following fixed rules.
• It’s like an office robot that presses buttons for you.
• It doesn’t “understand” what it’s doing → just follows predefined steps (if the screen changes, it breaks).

Treasury example: A bot that opens MT940 statements, copies data to Excel, and saves the file.

Agentic AI

• Receives an objective, creates its own plan of steps, and adapts if it encounters something different.
• Has “intelligence” (LLM + ML + feedback loop).
• Can decide independently: “I don’t have the bank statement today, I’ll search in another folder / send an alert.”

Treasury example: You tell it “I want the daily cash report.” It does this autonomously: searches for statements, performs calculations, checks for missing data, sends the report.

The Key Difference

• RPA = rigid executor → if you change the rule, it breaks
• Agentic AI = intelligent junior colleague → knows the final objective and can find another path if something doesn’t work

Conclusion: RPA is like a colleague who only knows how to copy-paste, but if we move the columns in Excel, they don’t know what to do anymore. Agentic AI is like a junior colleague who knows the final objective and if changes occur, finds another solution to deliver the result.

In practice: the two are not mutually exclusive. RPA does the mechanical execution; Agentic AI decides and orchestrates the steps.

How to Build an Agentic AI

1) Components (imagine LEGO Pieces)

• Objective (Goal): What it needs to deliver daily (“send cash report at 09:00”)
• Abilities/Tools: What the agent can concretely do (reads Excel, calls bank API, writes to SharePoint, sends email)
• Planner: Breaks the objective into steps and chooses the right order
• Executor (Actor): Runs the steps (executes flows, calls APIs, starts RPA)
• Memory: Remembers what worked yesterday (data sources, mappings, exceptions)
• Evaluator / Critic (Self-check): Verifies the output (“total balance = sum across entities?”)
• Rules / Guardrails: Limits and approvals (“don’t launch payments; only propose”)
• Audit & Log: Who, what, when — for compliance
• Interface (Chat/Buttons): How users interact (Teams, email, buttons)
• Orchestrator: Connects everything (decides which “tool” is called when)

2) Workflow (Agent Cycle)

Objective → Plan → Action → Verification → Decision (deliver / ask for help) → Memory (learn from exceptions)

3) Example: “Cash Reporting Agent”

• Objective: “At 09:00, send the cash report to the Teams group.”
• Abilities: Reads MT940/camt.053 files from folder, parses, calculates KPIs, updates Power BI, sends message
• Planner: “1) collect statements → 2) validate amounts → 3) calculate cash by entities/currencies → 4) publish → 5) notify”
• Executor: Starts Power Automate flow (import + transformation), then “Send Teams” action
• Evaluator: Checks: total cash = sum across all accounts? Missing banks? If yes → sends alert “missing ING FR file”
• Guardrails: Doesn’t modify source data, doesn’t launch payments. Only reads and reports.
• Memory: Remembers that “ING FR is often missing on Mondays” and tries the secondary source

4) How to “Assemble” It in Practice (Without Heavy Coding)

Option A — Microsoft Stack (Easy to Adopt)

• Copilot Studio: Define the agent (role, objective, messages)
• Power Automate (Cloud + Desktop): Create the flows (“Abilities”):
  • Import files (OneDrive/SharePoint/SFTP)
  • Transformation (Power Query)
  • Write results (Excel/Dataverse/SharePoint)
  • Notification (Outlook/Teams)
• Power BI: Dashboard + anomaly detection
• Guardrails: Approvals in Power Automate (Approval step) for sensitive steps
• Logging: Table in SharePoint/Dataverse with each execution (time, steps, status, errors)

Option B — No/Low-Code Mix (Cheap, Fast)

• Zapier / Make / n8n (open-source): Orchestration between email, storage, spreadsheets, Slack/Teams, etc
• LLM (ChatGPT/Claude API or Llama/Mistral via Ollama local): For planning, messages, summaries, etc.
• OCR/Tables: Google Drive OCR or Tesseract (free) for PDFs
• Logs: Google Sheets / Airtable as central journal

Option C — Open-Source Local (For Confidentiality)

• Ollama + Llama/Mistral (local LLM) = Planner/Evaluator
• n8n (orchestration) = Executor/Interfaces
• PostgreSQL/SQLite = Memory + Audit
• Keycloak/AAD = Access and roles

5) What You Need to “Describe” (Not Code)

Agent Charter (1 page):

• Purpose, limits, data sources, expected results, “human-in-the-loop” contact

Abilities List (tool catalog):

• You’ll have to define a list of skill where you explain the outcome expected. You name them, for example, get_bank_statements, parse_camt053, update_powerbi, send_teams_message, request_approval

Success/Stop Rules (acceptance criteria):

• “Don’t deliver report if >10% of accounts are missing”

Exception Rule:

• “If statements are missing: re-run; if still missing → alert + attach list of accounts without data”

6) How to Explain the “Intelligence” in the Agent

• Plans the order of steps independently (based on what it finds)
• Chooses tools (folder vs. API) if one doesn’t work
• Self-verifies (total sum vs. by entities; thresholds; anomalies)
• Learns minimally from history (remembers what failed and avoids the wrong route next time)
• Asks humans when the rule says it must (payments >100k, missing critical data)

7) Control & Compliance (Must Be Said on Stage)

• Least privilege: Read-only where possible
• Mandatory approvals for actions with impact (doesn’t execute payments)
• Complete journal (timestamp, input, output, who approved)
• Testing on synthetic data before production
• Explainability: Each decision has a reason (e.g., “1.8% difference vs. historical average for DE entity”)

8) KPIs to Demonstrate Value

• Report delivery time (from manual 90 min → automated <10 min)
• % executions without error
• Number of useful alerts / false positives
• Time saved / month
• Reconciliation accuracy / data completeness in report

9) 10-Day Pilot Plan

• Day 1–2: Define Agent Charter + sources/data + expected output
• Day 3–4: Create 2–3 Abilities in Power Automate (import/transformation/notification)
• Day 5: Add Verification Rules (total sum, missing items)
• Day 6: Add Teams bot (daily message + “Re-run” button)
• Day 7–8: Logging + approval for exceptions
• Day 9: Test on 30 days of history (non-sensitive data)
• Day 10: Limited go-live (2 entities), measure KPIs

10) Questions to Ask Vendors (Control Questions)

• “What concrete abilities does your agent expose?”
• “What memory do you use (where is it stored, how long, who sees it)?”
• “How does it self-check and what thresholds can I configure?”
• “Where are the guardrails (what is it NEVER allowed to do)?”
• “What does the audit log look like and how do I export for internal audit?”
• “Which parts are RPA (deterministic) vs. AI (adaptive) in your solution?”

RPA vs. Agentic AI in Treasury: Comparison Table

Process	RPA (Robotic Process Automation)	Agentic AI
Import bank statements	Runs macro: opens PDF/MT940 → copies to Excel → saves. If format changes, it breaks.	Understands the goal “import statements” → if file is different format, searches for another method (OCR, API) or sends alert.
Cash position reporting	Copies data daily into a fixed Excel and sends email.	Plans its steps: collects data, checks for missing items, calculates cash, explains deviations, and sends the report.
Reconciliation of payments vs. invoices	Matches rows by exact rules (e.g., Invoice 123 = Inv 123).	Can use NLP for fuzzy matching, understands context (“supplier X, similar invoice”), marks exceptions.
Cash-flow forecasting	RPA only runs a model from Excel and saves the result.	AI agent selects the right data, chooses the model (ARIMA/LSTM), validates prediction, and explains to CFO.
Payment control	Fixed rules: “if amount > 100k and not approved → block”.	Learns from history: “this payment looks different from normal behavior” → raises intelligent alert.
User interface	Doesn’t interact, only runs in background.	You can talk to it (“What large payments do we have today?”) and it responds like a copilot.

The Real State of Agentic AI in Treasury

At this stage, Agentic AI can be powerful assistants, but only up to a point. They can plan, verify, and adapt within predefined boundaries, yet they are not ready to operate with full autonomy in a treasury environment.
Technology today still requires human oversight for judgment, context interpretation, and exception handling, areas where financial reasoning, compliance, and accountability are critical.

Current agents can execute repetitive and semi-structured processes, such as reporting, reconciliation, or risk monitoring.
What they cannot yet do reliably is make discretionary decisions: deciding when to override a rule, interpret conflicting data sources, or handle situations that involve ambiguity or business nuance.

The right approach today is hybrid: let the Agentic AI handle execution and anomaly detection, while the human remains the decision-maker and ethical boundary. Over time, as memory, reasoning, and context-awareness mature, agents will move from “assistants” to “autonomous analysts.”

Until then, the intelligent treasury is not about replacing expertise. The winners will be those who learn to design and supervise these systems, not those who wait for them to become perfect.

Agentic AI is no longer a distant promise, but it’s also not the autopilot of finance yet. It’s the next generation of collaboration between humans and machines, one where autonomy is built step by step, not assumed.

Enjoy!