Devancore Inc.
Devancore Post-Trade Glossary
Glossary
Agentic AI Controls
Agentic AI controls define what an AI agent may read, propose, stage, execute, escalate, and record inside capital markets workflows.
Document source: https://devancore.com/glossary/agentic-ai-capital-markets-controls/
Devancore Post-Trade Glossary
Agentic AI Controls
Agentic AI controls define what an AI agent may read, propose, stage, execute, escalate, and record inside capital markets workflows.
Definition
Agentic AI controls are the permission, policy, review, monitoring, and evidence controls that govern AI agents in capital markets workflows. The issue is not whether an agent can call a tool. The issue is whether the firm can prove which tool it called, why it was allowed, what records it used, which state it proposed to change, who approved the action, and what the downstream system returned.
Capital markets workflows contain many action types with different risk levels. Reading a position is not the same as drafting an allocation correction. Drafting a settlement instruction is not the same as releasing it. Closing a reconciliation break is not the same as explaining why it exists. A control model that treats all tool calls as one permission class is too blunt for regulated operations.
Agent permission boundary
Agent permission boundary
Every agent capability should map to a permitted action class and evidence requirement.
| Capability | Allowed role | Control evidence |
|---|---|---|
| Read | Retrieve permitted records, statuses, policies, exceptions, and reports | User entitlement, source IDs, timestamps, and fields returned |
| Explain | Summarize facts, gaps, workflow state, and likely next steps | Citations, assumptions, confidence, and missing-evidence flags |
| Draft | Prepare notes, payloads, messages, corrections, or tasks without committing state | Draft ID, source records, schema result, and reviewer queue |
| Stage | Place a proposed workflow update behind a release gate | Policy check, maker-checker status, risk result, and required approver |
| Release | Commit only where policy allows and approval is complete | Approver, action payload, idempotency key, downstream response, and timestamp |
| Stop | Pause agent work, revoke tools, escalate, or route human takeover | Kill switch event, reason, open state, and recovery owner |
The useful design pattern is bounded agency. An agent may retrieve permitted records, classify work, assemble evidence, draft messages, prepare payloads, monitor statuses, and route tasks. Material state changes should pass through deterministic policy checks and accountable review. The boundary should be explicit in the product, the logs, and the supervisory procedure.
Tool scope is the core control. Each agent session should receive a task-specific registry: which records it can read, which fields it can see, which tools it can call, which payloads it can draft, which actions it can stage, and which actions require approval. The registry should reflect the user, role, account, portfolio, business line, environment, and workflow. The boundary should be enforced by the application layer and policy engine, not by prompt instructions alone. A prompt injection, malformed inbound message, or corrupted counterparty file should not be able to persuade the model to discover or request tools outside its approved scope.
Agentic controls also need an intervention path. If a model begins looping, using stale data, calling the wrong tool, producing unsupported citations, or approaching a restricted state change, the system should be able to pause the agent, revoke tools, freeze a workflow item, and route human takeover. The stop event itself becomes part of the operating record.
Agentic AI controls - unmanaged vs governed agency
Devancore · control state
Before
Unmanaged agent
- Broad tool access
- Shared credentials
- Model-selected actions
- Weak state evidence
- Manual reconstruction
After
Governed agent
- Least-privilege registry
- Scoped identity
- Policy and human gates
- System-of-record evidence
- Replayable audit trail
How it works
Agentic AI controls work by placing a control plane between model reasoning and operating systems. The user defines a goal. The control layer scopes identity and tools. The agent retrieves records, drafts a proposed next step, validates the proposal, routes approval where required, calls the permitted tool, monitors the response, and records the final state.
Agentic workflow controls
Agentic workflow controls
The control plane should constrain reasoning before any tool action changes operating state.
| Control layer | What it governs | Failure mode |
|---|---|---|
| Identity | User, agent, session, service principal, environment, and business role | Unattributed action or shared credential |
| Tool scope | Read, draft, stage, release, export, message, reconcile, or report capability | Prompt injection or corrupted input tries to expand the agent's tools |
| Policy gate | Limits, WSP rules, risk thresholds, account eligibility, data freshness, and required evidence | Reasonable-looking action bypasses deterministic checks |
| Human gate | Maker-checker, supervisor review, WSP routing, override reason, and approval authority | Agent commits a material state change without accountable review |
| Execution monitor | Tool-call status, retries, duplicate suppression, timeout, cancellation, and downstream response | Runaway loop, duplicate action, or orphaned task |
| Audit trail | Prompt, retrieved context, tool arguments, validation, approval, supervisory sample state, outcome, and final record state | Unreconstructable agent activity |
Identity comes first. The record should distinguish the human user, the agent identity, the service credential, the session, the environment, and the business role. Shared credentials weaken accountability because a reviewer cannot tell whether an action came from a user, a model, a scheduled job, or a downstream system.
Tool scope should be dynamic. A user investigating a settlement fail may need read access to trade, SSI, custodian, clearing, cash, and exception records. That same user may not need order release, ledger adjustment, journal posting, or client communication tools in the agent session. Hiding the wrong tools is a control, not a UX preference. The agent should not be allowed to escalate its own authority, even if a user prompt, SWIFT message, ISO message, email, or file payload contains instructions that appear to request it.
Policy gates translate firm rules into machine checks. The workflow should test account eligibility, security restrictions, stale data, missing fields, notional thresholds, settlement risk, WSP routing, maker-checker requirements, and record-retention needs before an action is staged or released. Model confidence should not override those checks.
Human gates apply where judgment, authority, or material state change is involved. A reviewer should see the original prompt, retrieved sources, tool registry, proposed payload, validation result, warnings, and expected downstream effect. Broker-dealer use should be reflected in the firm's written supervisory procedures where the agent participates in supervised activity, so supervisors have a reviewable interface for sampling paths, approvals, holds, overrides, and rejected actions. If a reviewer overrides a warning, the reason should be captured before release.
Execution monitoring protects against agent loops and orphaned work. Tool calls should carry request IDs, idempotency keys where available, retry limits, timeout handling, cancellation state, and downstream response capture. A failed or partial response should create an owned work item, not leave the agent to improvise.
The audit trail is the product of the control plane. It should preserve the question, goal, retrieved context, tool arguments, validation result, draft, approval, release event, downstream response, exception state, and final operating record. Broker-dealer environments may need this evidence to align with FINRA Rule 3110-style supervision, written supervisory procedures, books-and-records policy, and AI governance review.
Agentic AI controls - tool action path
Devancore Glossary · devancore.com
Agentic AI controls - tool action path
Devancore Glossary · devancore.com
In Devancore™
Agentic AI controls - capital markets risk register
Devancore · risk register
Tool overreach
highCause The agent receives tools beyond the user's role, account, workflow, or environment.
Control Use a least-privilege registry scoped by user, task, business line, and action class.
Silent state change
highCause A staged correction, settlement instruction, or order action commits without approval evidence.
Control Separate draft, stage, approval, release, and downstream confirmation states.
Runaway loop
highCause The agent retries failed calls, reopens tasks, or repeats actions after timeouts.
Control Apply idempotency, retry limits, cancellation controls, and kill-switch monitoring.
Weak grounding
mediumCause The agent explains an action from stale, incomplete, or uncited records while cash, inventory, and matching state keep changing intraday.
Control Require source IDs, as-of timestamps, freshness checks, validation results, and missing-evidence warnings.
Unclear accountability
mediumCause The record cannot distinguish user intent, agent proposal, reviewer approval, and system response.
Control Log actor identity, agent identity, prompt, tool payload, approval, and final state separately.
Devancore supports agentic AI controls as governed operating-record infrastructure for capital markets workflows. It can help connect user intent, agent identity, tool scope, source records, policy checks, approval states, downstream system responses, exception handling, and audit trail.
Devancore should not be framed as an autonomous trader, broker, custodian, clearing broker, compliance officer, accounting authority, legal adviser, or execution venue. Its role is to help firms govern AI-assisted workflow activity around the systems and people that remain accountable.
In a Devancore-style workflow, the agent starts with a bounded task. The platform scopes tools, retrieves permitted records, validates the proposed action, holds material changes for review, records the human decision, and attaches the downstream response. If the agent hits stale data, missing evidence, an entitlement block, a policy breach, or a retry limit, the workflow moves to a held state with owner, reason, and next action.
This page complements AI agent post-trade operations and MCP trading integration. AI agent post-trade operations describes the tasks agents can assist with. MCP trading integration describes the tool interface. Agentic AI controls defines the governance layer that decides what the agent may do, what it may only propose, and what must be reviewed before operating state changes.
The standard is simple: bounded tools, deterministic gates, human accountability, and reconstructable evidence. If any of those is missing, agentic workflow becomes operational risk instead of controlled operating leverage.
Related terms
- Operational Risk Management Securities
https://devancore.com/glossary/operational-risk-management-securities/
The identification and mitigation of risks from failed processes, human errors, technology failures, and external events that disrupt securities operations or cause financial loss.
- Maker-Checker Workflow
https://devancore.com/glossary/maker-checker-workflow/
A two-person segregation of duties control requiring that any action entered by one operator must be reviewed and approved by a second before it takes effect.
- Segregation of Duties (SoD)
https://devancore.com/glossary/segregation-of-duties-financial-software/
Segregation of duties (SoD) is the internal control principle that no single operator can book, approve, and settle a transaction — enforced through conflict matrices, maker-checker workflows, and access certification reviews to satisfy SOX Section 404.
- Post-Trade Compliance Software
https://devancore.com/glossary/post-trade-compliance-software/
The technology layer that turns post-trade activity into an exam-ready compliance record: audit trail, supervisory controls, and books and records under SEC and FINRA rules.
- Broker-Dealer Audit Trail
https://devancore.com/glossary/broker-dealer-audit-trail/
The immutable, chronologically linked record of every trade lifecycle event — from order receipt through settlement — maintained to satisfy SEC Rules 17a-3 and 17a-4, FINRA clock synchronization requirements, and CAT reporting obligations.
- System of Record Securities Operations
https://devancore.com/glossary/system-of-record-securities-operations/
The authoritative single source of truth for a firm's positions, trades, and accounts — the system of record that all other systems, reports, and compliance functions derive from.
- AI Agent Post-Trade Ops
https://devancore.com/glossary/ai-agent-post-trade-operations/
AI agent post-trade operations use controlled agents to detect exceptions, assemble evidence, draft actions, route approvals, and record outcomes without bypassing human supervision.
- MCP Trading Integration
https://devancore.com/glossary/mcp-trading-integration/
MCP trading integration connects AI assistants to trading, account, portfolio, and post-trade tools through governed context, permissioned tool calls, staged actions, and audit evidence.
- AI Audit Trail Financial Services
https://devancore.com/glossary/ai-audit-trail-financial-services/
An AI audit trail in financial services records the prompt, permissions, retrieved context, model output, proposed action, human decision, downstream event, and final outcome for each AI-assisted workflow.
- Human-in-the-Loop Execution
https://devancore.com/glossary/human-in-the-loop-trade-execution/
Human-in-the-loop trade execution is a controlled workflow where software can draft, enrich, validate, or prepare an order, but a human approval state is required before release.
- AI Compliance Assistant
https://devancore.com/glossary/ai-compliance-copilot-broker-dealer/
An AI compliance assistant helps broker-dealer teams find records, summarize exceptions, assemble evidence, compare activity to procedures, and route review without replacing supervisory responsibility.
- AI Brokerage API Workflow
https://devancore.com/glossary/ai-brokerage-api-workflow/
AI brokerage API workflow is the controlled use of AI to retrieve brokerage account context, draft API actions, monitor order state, and preserve evidence without bypassing approval controls.
- Asset Servicing Data Controls
https://devancore.com/glossary/asset-servicing-data-controls/
Asset servicing data controls govern the event layer on held positions: notifications, dates, entitlements, elections, income, tax attributes, and the evidence that custody and accounting consume.
- Fixed Income OMS Post Trade Controls
https://devancore.com/glossary/fixed-income-oms-post-trade-controls/
Fixed income OMS post-trade controls verify bond trade records after execution across price, yield, accrued interest, coupon terms, maturity, settlement path, allocation, confirmation, reporting, and reconciliation.
- Outsourced Execution Post Trade Controls
https://devancore.com/glossary/outsourced-execution-post-trade-controls/
Outsourced execution post-trade controls verify fills, allocations, confirmations, settlement data, exceptions, and books-and-records evidence after an external desk executes a trade.
- Outsourced Trading Controls
https://devancore.com/glossary/outsourced-trading-controls/
Outsourced trading controls are the oversight records, workflow checks, approvals, and evidence used when an investment manager delegates execution activity but keeps responsibility for trade records and supervision.
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