Why final-output review is insufficient
CRISIL’s 2026 paper on agentic financial workflows argues that institutions must control the complete execution trajectory because agents can plan, retrieve information, invoke tools and act across multi-step workflows. A correct final answer does not prove that every intermediate action stayed within mandate (CRISIL, 2026).
This is a material shift from conventional model validation. The unit of assurance expands from model output to a sequence of state changes: what the agent knew, what it inferred, which tool it chose, what permissions applied and what action followed.
Banking increases the importance of authority
Banking workflows combine regulated data, customer impact, monetary actions and formal control responsibilities. Deloitte’s 2026 analysis of agentic AI in banking highlights the need for stronger oversight as institutions allocate budgets and begin introducing agents into operational processes (Deloitte, 2026).
An agent that drafts a customer response is not equivalent to an agent that changes a limit, initiates a payment or updates a regulatory record. Architecture should therefore bind authority to the action, not merely to the user session.
- 01Request
- 02Plan
- 03Retrieve evidence
- 04Policy check
- 05Tool action
- 06Approval / containment
- 07Audit trail
The control object is the execution trajectory, not only the final response.
Runtime policy becomes part of the platform
A robust design separates planning from execution. The model can propose an action, but a policy layer independently evaluates identity, entitlement, data sensitivity, transaction limits, segregation-of-duties rules and approval requirements before the tool call is executed.
The Financial Stability Board’s 2026 sound-practices consultation reinforces the broader need for organisation-wide governance across the AI lifecycle. Agentic workflows add a runtime dimension to that lifecycle: controls must be continuously enforceable while the system acts (FSB, 2026).
Arvec view: every agent action should be reconstructable
For high-consequence banking workflows, auditability should answer five questions: what objective was pursued, what evidence was used, what authority existed, what action occurred and why escalation did or did not happen.
This leads naturally to decision architecture. Argument Vectors make the decision context explicit; runtime telemetry then proves whether the agent remained inside that context.
Key implications
- 01
Record agent plans, evidence, tool calls and policy decisions as one audit trajectory.
- 02
Separate model reasoning from the policy engine that authorises actions.
- 03
Bind entitlements and transaction limits to tools and actions.
- 04
Design containment and human escalation before increasing autonomy.
Related AIF7™ capabilities
AI Governance & Trust
Defines mandate, approval, containment and auditability.
AI Engineering & Delivery
Implements policy enforcement and trajectory telemetry.
AI Data Foundation
Controls what evidence and sensitive information the agent can access.
AI Organisation & Operating Model
Allocates accountability for agent actions and exceptions.
Sources & further reading
01 · CRISIL 2026
Governing the Agentic Frontier: Runtime Validation and Governance Frameworks for AI Driven Financial WorkflowsCRISIL · 7 August 2026
02 · Deloitte 2026
Managing the new wave of risks from AI agents in bankingDeloitte Center for Financial Services · 5 March 2026
03 · FSB 2026
Sound Practices for Responsible Adoption of Artificial Intelligence: Consultation reportFinancial Stability Board · 10 June 2026