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Devancore Post-Trade Glossary

AI Agent Post-Trade Ops

AI agent post-trade operations use controlled agents to detect exceptions, assemble evidence, draft actions, route approvals, and record outcomes without bypassing human supervision.

Definition

AI agent post-trade operations are controlled workflows where an agent helps post-trade teams move from exception detection to prepared action. The agent may find a break, retrieve evidence, compare records, classify a reason, draft a correction, route an approval, or explain a settlement state. The final state change still belongs to the governed workflow.

The distinction from conversational finance matters. Conversational finance asks questions over entitled records. An AI agent can take the next operating step: investigate, assemble, draft, and route. That makes the control design stricter. The more the agent can prepare, the more clearly the firm must define what it may read, what it may propose, what it may not post, and how its work is recorded.

Agent work to controlled record

Agent work to controlled record

The useful agent output is the record it prepares, not the fluent explanation.

Agent task Source records Control question
Detect Trade blotter, settlement status, reconciliation breaks, cash, positions, and exception queues What exception or risk state triggered the agent?
Investigate Confirmations, allocations, custodian files, messages, FIX logs, statements, and prior workflow events Can the agent show the evidence behind its finding?
Classify Reason codes, tolerances, account scope, counterparty, security, amount, and aging Is the issue a timing item, data break, settlement fail, or control exception?
Draft Correction template, approval policy, user role, downstream API, and record status Is the proposed action complete but not silently posted?
Route Owner, supervisor, maker-checker rule, escalation path, and service-level threshold Who has authority to approve or reject the proposed action?
Record Prompt, retrieved keys, citations, proposal, approver, instruction, and outcome Can the agent's work be reconstructed later?

Post-trade work is record-bound. A trade break, settlement fail, cash mismatch, position difference, unmatched confirmation, stale price, or missing instruction is not solved by text. It is solved when the relevant source records are identified, compared, classified, corrected or escalated, and closed with evidence.

The agent should therefore be treated as a preparer. It can reduce search time, gather fragments, summarize evidence, and draft the next step. It should not become an invisible actor changing books, approving exceptions, submitting instructions, or overriding controls without the same entitlement and review that would apply to a human workflow.

The operating record is the center. If the agent proposes a break resolution, the record should show the prompt or trigger, retrieved records, source keys, as-of times, logic summary, proposed action, user, checker, downstream event, and final outcome. Without that chain, the agent creates a new exception while trying to close the old one.

How it works

AI agent post-trade operations work by separating investigation from state change. The agent can help investigate and draft. The system of record, approval workflow, and downstream API determine whether anything changes.

Controlled agent workflow

Controlled agent workflow

Post-trade agents should prepare work, not erase controls.

Step Data required Failure mode
Receive trigger Break, fail, unmatched confirmation, stale record, aged exception, or user request The agent starts from a vague prompt rather than a named operating event
Check entitlement User role, account scope, record permissions, action rights, and downstream system access The agent retrieves or proposes work outside the user's authority
Retrieve evidence Source records, documents, messages, transaction IDs, as-of timestamps, and lineage The explanation cannot be tied to source rows
Draft action Reason code, proposed correction, message text, workflow target, affected records, and expected outcome The agent writes a plausible action without enough evidence
Route approval Maker-checker rule, approver, escalation policy, materiality, and time sensitivity A state change bypasses review because the agent appears confident
Capture outcome Approval, rejection, posting event, instruction ID, downstream response, and closed evidence package The firm cannot replay what the agent did and why

The workflow starts with a trigger. That trigger may be a user question, trade break, settlement fail, unmatched confirmation, cash difference, aged exception, or stale operating record. A controlled agent should start from a named event, not from a broad request to "fix post-trade."

Entitlement comes next. The agent should inherit the same access boundaries that apply to the user or workflow. If the user cannot see a portfolio, account, custodian file, counterparty, or exception queue in the source system, the agent should not retrieve or summarize it.

Evidence retrieval is the agent's practical value. It may collect confirmations, allocations, FIX logs, SWIFT messages, custodian files, broker comments, emails, PDFs, workflow notes, prior exceptions, and source records. The answer should retain citations to those records, not only a narrative explanation.

Drafting is the controlled action boundary. The agent may draft a correction, message, escalation, reason code, owner assignment, or proposed settlement repair. That draft should remain visibly different from a posted record. The checker should see what the agent used, what it concluded, and what will change if approved.

Approval and outcome close the workflow. A maker-checker rule, supervisor review, service-level policy, or downstream system permission decides whether the draft proceeds. The final record should preserve approval, rejection, posting event, instruction ID, downstream response, and closed evidence package.

This is why agentic post-trade work should avoid autonomous language. Institutional operations need prepared work with controls. They do not need an opaque actor creating or changing records outside supervision.

In Devancore™

Devancore supports AI agent post-trade operations as a controlled operating-record and workflow layer. It can help connect questions, triggers, source records, citations, draft actions, approvals, downstream responses, and audit trail around post-trade work.

Devancore should not be framed as an autonomous trader, execution venue, broker, custodian, clearing broker, adviser, compliance officer, or accounting authority. Its role is to support the record and workflow around post-trade tasks: trade capture, enrichment, confirmation, reconciliation, settlement status, exception management, evidence assembly, and reporting inputs.

In a Devancore-style workflow, the agent starts with a named event. A failed trade, cash break, unmatched confirmation, stale position, or user request becomes a controlled task. The agent retrieves entitled evidence, drafts an action, and routes it into the approval path. The record shows what was read, what was proposed, who approved, what posted, and what remained open.

This matters when operations teams use agents across traditional and digital asset workflows. The agent may inspect a trade record, custodian status, settlement instruction, on-chain event, cash movement, or reconciliation break. The same principle applies: source evidence first, action boundary second, approval third, audit trail always.

Conversational finance gives the user a controlled way to ask. AI agent post-trade operations give the system a controlled way to prepare. Devancore's value is keeping both attached to records that operations, compliance, finance, and supervisors can review.