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Use an LLM to interpret requests, extract information, or propose a next step—but let deterministic code control permissions, workflow state, validation, approvals, and consequential actions. This can make the process reviewable and controlled; it does not make the model’s reasoning reproducible, correct, or safe by itself.
Separate model reasoning from authority to act
A financial agent may need to handle requests that vary in wording or context. That flexibility is useful for tasks such as classifying a request, extracting fields, summarizing evidence, or drafting a recommendation. It is a poor reason to let the model decide, without independent checks, whether money moves, a record changes, or a customer receives a consequential decision.
Microsoft’s Durable Task guidance distinguishes a deterministic workflow from an agent loop: in a deterministic workflow, code controls the execution path, while non-deterministic operations—such as LLM calls, tool use, and API requests—run inside bounded activities. The workflow owns sequencing, branching, error handling, and state transitions. In Microsoft’s words, “In a deterministic workflow, your code controls the execution path.”
In practice, the model’s answer should be treated as a proposal or intermediate result. Ordinary code should check it against an expected schema, applicable policy, the caller’s permissions, business rules, and current system state before allowing any transition. A plausible explanation from the model is not an authorization, and a model must not be able to grant itself new authority through its response.
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Choose orchestration to match the workflow’s risk and uncertainty
Decide how much control the process needs by looking at how predictable its steps are, how much adaptation is genuinely required, how closely reviewers must inspect the path, and what an error could do. An agent loop can be useful for open-ended tasks that require selecting tools or adapting to intermediate results. A hybrid can use that flexibility inside bounded activities while code retains control over permissions and consequential state changes.
| Architecture | How the path is controlled | When it fits | Financial-workflow implication |
|---|---|---|---|
| Deterministic workflow | Code sets the sequence, branches, and error handling; model calls and tool requests run as activities. | Steps are known, explicit guardrails are needed, or reviewers need a clear control path. | Useful when an action must pass defined checks or approval gates before execution. |
| Agent loop | The agent adapts its next step or tool choice in response to intermediate results. | The task is open-ended and needs more adaptation than a fixed sequence provides. | Bound its tools and scope; do not treat agent-selected actions as financial authorization. |
| Hybrid | Deterministic orchestration controls state and permissions; an agent operates within a bounded activity. | A workflow needs flexible interpretation but must keep consequential transitions reviewable. | Lets the model propose or investigate while controlled code gates any permitted action. |
AWS describes prompt chaining, routing, parallelization, orchestrator-worker, and evaluator-refinement as composable patterns. They can organize reasoning and coordination, but using one does not establish authorization to act. Design permission checks and execution controls around the pattern rather than assuming the pattern supplies them.
Put explicit gates between a model response and a financial action
A practical workflow separates interpretation from execution. Its exact sequence depends on the task, but each consequential transition should have an identifiable owner and a checkable basis.
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- Set the scope. Resolve the allowed task, data access, and agent identity. Give the agent only the tools and operations needed for this workflow.
- Ask for a bounded result. Use the model for a specific interpretation, extraction, summary, or proposal—not an unrestricted mandate to complete a financial objective.
- Validate independently. Check response format and required fields, evidence references where applicable, policy constraints, business rules, permissions, and current state in ordinary code.
- Route exceptions for review. Send high-risk, irreversible, ambiguous, or out-of-policy cases to an authorized human reviewer. Keep approval separate from the model’s recommendation.
- Execute only an allowed action. Use an authorized tool, recheck relevant state, and use idempotency protections where available to reduce the chance that retries duplicate an action.
- Record the outcome. Capture the decision path, checks, approvals, and result so the execution can be monitored and investigated.
Validation should fail closed for prohibited actions: if required data is missing, a permission check fails, or the result does not meet the expected format, the workflow should not silently continue. Microsoft recommends deterministic controls that block prohibited actions regardless of model output, as well as safe ways to pause or stop execution and visibility into planned actions.
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Limit what an agent can access, decide, and change
Least privilege applies to the agent identity, its tools, and its data—not just to the human who initiated the request. Scope access to the task, restrict operations to what is necessary, and avoid giving a model a broad credential that can bypass workflow checks. For critical actions, AWS’s financial-services guidance points to supervision, segregation of duties, and maker-checker verification: one component or role proposes or prepares an action, while an appropriately authorized reviewer or control verifies it.
- Require approval for high-risk or irreversible actions, with the approver and decision recorded.
- Provide a dependable pause or stop mechanism that does not rely on the model deciding to stop.
- Make planned actions and relevant context visible to the people responsible for oversight.
- Consider prompt injection, sensitive-data leakage, inadequate human oversight, supply-chain compromise, and agent sprawl in the threat model.
These are architectural control considerations, not a claim that a particular design meets every legal or regulatory obligation. AWS notes that requirements vary by jurisdiction and use case; institutions should involve their legal and compliance teams when determining applicable controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the audit trail tell the execution story
A final model answer alone cannot show why a workflow took an action. Preserve enough linked context to reconstruct what happened, including where the system stopped or escalated. Useful trace fields include:
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- Request, caller, and agent identity, with relevant timestamps.
- Model and configuration version, plus the prompt or task context needed to interpret the run.
- References to retrieved data, rather than unnecessarily duplicating sensitive material.
- Tool calls and results, policy checks, validation outcomes, and workflow state transitions.
- Human approvals, overrides, escalations, the resulting action, and its outcome.
AWS calls for tracing decisions, actions, workflow activity, and caller context; Microsoft recommends accessible logs of actions, tools, and outcomes; KPMG’s financial-reporting guidance raises how agent actions are retained and reviewed for investigation. Those sources do not establish a universal retention period. Determine retention and regulated-record requirements for the relevant institution, jurisdiction, and use case.
Control model, prompt, tool, and workflow changes
The deployed system is more than its model. Changes to prompts, models, tools, data, routing, policies, or orchestration can change what the agent proposes and what the workflow permits. Treat changes to these components as changes to a controlled system: review them, validate expected behavior, obtain required approvals, and monitor after rollout.
Before deployment or a material update, test both permitted and prohibited cases, including failure paths and cases that should require human review. Keep version context with executions so a historical run can be interpreted against the components that actually produced it. KPMG’s 2026 financial-reporting guidance highlights review and approval of model and agent updates, monitoring exceptions and performance, checking segregation-of-duties conflicts, and identifying behavior changes caused by model, data, orchestration, or routing changes. AWS also recommends standardized evaluation frameworks and test harnesses.
Use financial-services standards and examples as inputs, not compliance proof
FINOS’s agentic financial-services resources point to ecosystem work including the Common Domain Model for shared trade and event representations, BPMN/DMN orchestration for permissions and human-in-the-loop controls, FDC3 for deterministic action-oriented tools, an AI Governance Framework, and TraderX as a spec-driven reference trading application. These can inform design choices and shared representations. Their existence does not prove that an implementation satisfies a particular institution’s policies or regulatory obligations.
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