Cash reconciliation agents
Match bank statements to ERP sub-ledgers, auto-resolve the easy breaks and queue the rest with a drafted explanation. The close gets shorter, not the control.
AI Services · Agentic AI
Agents that do the treasury work nobody misses doing
Multi-step agents for reconciliation, variance triage, approvals and confirmations — built on your stack, scoped to your policy, auditable to the same standard as the rest of treasury.
70%
Faster close cycle
Typical reduction in reconciliation time once agents take the routine breaks.
24/7
Confirmation coverage
Agents chase and parse overnight, so Monday morning starts from a clean queue.
100%
Audit trail
Every action logged against the transaction — nothing relies on someone remembering.
Orchestration in practice
One brief in. A close cycle out.
Specialised agents run in parallel — each scoped to one task, each traceable, each stoppable. The orchestrator plans, fans out, and hands the finished package to the human reviewer.
Orchestrating a close cycle
close-cycle.log
> Run month-end close for EMEA entities.● Plan: 4 parallel tasks, 1 human checkpoint.● Spawn(agent=reconcile, banks=[HSBC, JPM, Citi])↳ reconciled 1,219 lines · 0 breaks● Spawn(agent=variance, vs=forecast_v3)↳17 lines flagged >5% variance● Spawn(agent=fx_summary, entities=[DE, UK, CH])↳EUR/USD net +€1.9m · GBP/USD -€0.4m● Spawn(agent=chase, counterparty=BNP)⚠ Confirmation still open. Escalating.> Review package ready. Awaiting sign-off.
Agent 1
Reconciling the bank position
Agent 2
Drafting the variance note
Agent 3
Summarising FX exposure
Agent 4
Chasing a missing confirmation
Capabilities
Where agentic AI earns its keep
Not generic AI assistants. Purpose-built agents wired into the specific upstream and downstream systems each task depends on, with guard rails drawn by the risk owner — not by the vendor.
Forecast variance triage
Every actual-vs-forecast miss investigated, classified (timing, FX, one-off, structural) and summarised before the Monday review.
Approval drafting
Payment, hedge designation and intercompany loan approvals drafted against your policy, with evidence attached. Sign-offs stay with the humans.
Confirmation chasing
Counterparty confirmations, MTM statements and broker tickets chased, parsed and filed. Exceptions escalated by SLA, not by memory.
Policy & limit monitoring
Counterparty exposure, investment concentration and hedge coverage ratios watched against policy. Breaches flagged with context.
Audit-ready memory
Every tool call, model response and human override captured against the transaction. Reconstructable six quarters later.
What an agent actually is
Retrieve · act · observe · remember
Anthropic's Building Effective Agents is direct about it: the useful pattern is a closed loop. A model pulls context, calls a tool, observes the result, updates its memory, and repeats until the task is closed or a human stops it.
In treasury that means: retrieval from your data warehouse and policy store, tool-use against TMS / ERP / bank APIs, observation through audit-logged results, memory across runs so behaviour improves. Every hop traced.
Retrieval·Tool use·Observation·Memory
Adapted from Anthropic, Building Effective Agents. Teal edges feed the LLM; amber edges are tool calls and environment feedback.
Reference architecture
Orchestration
Copilot Studio · Semantic Kernel · LangGraph
Runtime
Azure AI Foundry · Managed Identity · Private Endpoints
Controls
Entra ID · Purview · Sentinel · Policy as code
Architecture
How the agents actually run
Three layers — orchestration makes the agent useful, runtime keeps it inside your data perimeter, controls make it safe to put in production. Composed from the same Microsoft building blocks your IT function already runs.
- Tool use gated by role and scope — agents only touch what their operator would touch
- Deterministic branches for policy-bound decisions, models used only where judgement helps
- Replayable execution traces for every run — reconstructable at audit six quarters later
- Kill switch and rollback on every deployed agent — always one click from a known-good state
Autonomous where it is boring, paranoid where it matters, and auditable all the way through.
Build & run
Three stages from workshop to live operation
Scope & design
Workshop the process with the risk owner. Map the decision tree, the data the agent will need, and the points where a human must stay in the loop.
Build & evaluate
Agent built in Copilot Studio or Semantic Kernel. Evaluation harness run against real historical cases — agents ship only when they beat the current baseline.
Run & govern
Production monitoring for drift, cost and override-rate. Quarterly red-team review. Model inventory and governance paperwork kept current automatically.
Start a conversation
Ready to put your treasury to work?
Book a 30-minute diagnostic call. We'll tell you within the hour whether we can help, and where the biggest wins likely sit.
Or email us directly at [email protected]