Workflow-Scoped Agent Design
Agents built against your specific process, such as reconciliation, invoice matching or case triage, not a general-purpose framework retrofitted to your workflow.
Service
AI agents & workflow automation
We build agents that do defined work inside your existing systems, such as reconciliation, document review, alert triage and first-draft reporting, with a human in the loop exactly where an error would cost you, and full automation where it would not.
What it is
"AI agent" has become a loose term for anything from a chatbot with a plugin to a fully autonomous system making decisions unsupervised. In back-office finance, the useful version sits between those two: a system that can plan a multi-step task, call the tools and APIs it needs, and hand off to a person at a clearly defined checkpoint, rather than either requiring a human for every step or running unsupervised on decisions that carry real financial or regulatory weight.
We build these against the specific workflow, not a generic agent framework wrapped around your ticket queue. That means understanding where a wrong answer is cheap to catch and reverse, such as routing a support query or drafting a first-pass summary, versus where it is expensive, such as approving a wire, closing a reconciliation break, or filing a regulatory report, and designing the automation boundary accordingly. The output is usually a mix: a fully automated straight-through path for the high-confidence, low-stakes majority of cases, and a structured escalation path with the evidence trail attached for the exceptions.
This lands most often in reconciliation, AP/AR processing, alert and case triage for fraud or AML teams, and first-draft reporting or variance commentary for FP&A. In every case the agent's reasoning and the sources it used are logged and retrievable, because in finance an automated decision nobody can explain after the fact is a liability regardless of how accurate it usually is.
What we build
Capabilities inside AI Agents & Workflow Automation
02
Human-in-the-Loop Checkpoints
Explicit sign-off steps designed around where an error is actually expensive, so review effort concentrates on the cases that need it instead of every case.
03
Straight-Through Processing
Automated handling for the high-confidence majority of routine cases, with the threshold tuned against a false-positive / false-negative budget you set, not a default.
04
Tool Use & System Integration
Agents that call your existing APIs and systems, such as the ledger, document store or case-management tool, rather than requiring a parallel system to run alongside them.
05
Exception Handling & Escalation
A structured path for cases the agent can't resolve confidently, with the evidence and reasoning trail attached so a human reviewer isn't starting from zero.
06
Audit Trail & Explainability
Every automated decision logged with the inputs, the reasoning steps and the sources used, retrievable for an internal audit or a regulator's question.
07
Multi-Agent Orchestration
For workflows with several distinct steps, such as extract, match, verify and route, a coordinated set of narrow agents works better than one agent trying to do everything, which tends to fail unpredictably.
08
Monitoring & Drift Detection
Ongoing tracking of automation rate, exception rate and accuracy, so a quietly degrading agent is caught by a dashboard rather than a downstream error.
How we work
Delivery process
01Process mapping
Document the current manual workflow end to end, including the judgment calls a person currently makes that aren't written down anywhere.
02Automation boundary design
Decide, case by case, where full automation is appropriate and where a checkpoint is required, based on cost of error rather than technical convenience.
03Agent build & tool integration
Build the agent against your real systems and data, with the tool calls and API integrations the workflow actually needs.
04Evaluation against historical cases
Test the agent against a set of your own historical cases with known outcomes, measuring accuracy and the automation rate at your chosen confidence threshold.
05Pilot & threshold tuning
Run in shadow or limited-production mode, tuning the straight-through threshold against real outcomes before full rollout.
06Rollout & monitoring
Full deployment with dashboards for automation rate, exception rate and drift, and a defined process for when thresholds need revisiting.
What to expect
6‑10 weeks
to a first agent handling real cases in shadow or limited production
40‑70%
typical straight-through rate for a well-scoped reconciliation or triage workflow, tuned to your risk tolerance
100% logged
every automated decision retains its inputs and reasoning trail for audit
Frequently asked questions
How do you decide what the agent can do without human sign-off?
By cost of error, not by what's technically possible. We work with your team to set the boundary, often expressed as a confidence threshold and a monetary or risk cap, and the agent escalates anything outside it. That boundary is documented and adjustable, not a black box.
What happens when the agent is wrong?
The exception path is designed for this: a flagged case with the agent's reasoning and sources attached, routed to a reviewer, and a feedback loop so recurring failure patterns get fixed rather than repeatedly escalated.
Does this fall under the EU AI Act?
Depends on the workflow. A credit-decisioning agent typically falls under Annex III high-risk obligations; a fraud or AML triage agent usually does not, since the Act specifically excludes financial-fraud detection from that credit-scoring category. Either way we build in the logging, human-oversight and documentation controls Annex III requires, because they're good practice for an auditable financial decision regardless of the formal classification. We'll map your specific workflow during scoping rather than assuming.
Can this integrate with our existing case-management or ledger system?
Yes. The agent is built against your actual systems and their APIs. Where a system has no API, we've built against screen automation as an interim step, though that's a fallback, not the target architecture.
How do we know the automation rate won't drift down in quality over time as volume changes?
Monitoring tracks automation rate, exception rate and accuracy against the original eval set on a rolling basis, with alerting if the pattern shifts. A quarter-end volume spike or a change upstream in the data feed shows up as a metric, not as a surprise three weeks later.
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Industries we apply this in
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