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Use Cases

Agentic-first ContextOS use-case playbooks for governed enterprise work.

Living DocumentLast reviewed: Edit on GitHub
At a glance

Use cases are reusable operating patterns for agentic work, not one-off demos. Each playbook starts from the same canonical execution contract: compile a Context Pack, produce a typed plan, verify a decision checkpoint before governed action, execute through the Tool Gateway, and emit a replayable DecisionRecord.

The strongest ContextOS use cases are not consumer assistants or content workflows. They are high-context, cross-system, evidence-heavy workflows where agents must coordinate work, use tools, respect policy, ask for approval, and preserve a record that an operator, auditor, or reviewer can replay.

Agentic-first playbooks

Agentic Incident Command Center

Coordinate signal, diagnosis, remediation, communications, and guardian agents across APM, SIEM, cloud, IAM, tickets, and status pages.

incident.triage -> remediation.execute -> customer_update.publish

Software Delivery Squad

Run human-supervised software agents through ticket analysis, codebase search, scoped edits, validation, review, and PR creation.

plan.approve -> patch.prepare -> pr.open -> deploy.request

Financial Crime Operations

Support KYC, AML, sanctions, and fraud casework with evidence agents, policy agents, narrative agents, and human adjudication gates.

alert.triage -> evidence.gather -> case.disposition -> report.file

Enterprise Data Stewardship

Detect schema, lineage, quality, privacy, and semantic drift, then route production changes through steward and owner gates.

drift.classify -> quality.remediate -> lineage.certify

Regulated Back-Office Workflows

Assemble evidence, reconcile inconsistencies, draft artifacts, route approvals, and execute follow-ups across finance, legal, operations, and compliance.

pack.assemble -> variance.explain -> followup.execute -> regulatory_submit

Selection criteria

Use ContextOS when a workflow has at least three of these traits:

TraitWhy ContextOS matters
Multi-agent workDifferent agents gather evidence, reason, draft, verify, and execute.
Cross-system toolsThe run touches operational systems, not just chat or retrieval.
Policy-bound actionSome steps need allow, deny, escalate, or approval decisions.
Evidence-heavy decisionsA human or auditor must see why the agent did what it did.
Durable side effectsThe workflow changes systems, sends messages, opens PRs, files reports, or updates records.
Replay requirementTeams need to debug, audit, or improve the decision later.

Reusable pattern

Use the same small pattern when adding a new playbook:

StageDeclare
Intentcanonical intent name and the Decision Specs it may emit
Context Packpolicy bundles, adapter permissions, evidence sources, memory write policy
Plantyped steps, dependencies, approval-mode tier per governed step
Checkpointthe decision_id, required evidence, and checkpoint before any delegated or destructive action
Gatewayadapter/tool IDs, argument constraints, idempotency requirements, approval gate names
RecordDecisionRecord outputs, evidence_refs, approvals, controls, trace, and memory proposals

Research base

The playbooks are grounded in public enterprise-agent patterns:

  • Gartner predicts rapid growth of task-specific enterprise agents and names cybersecurity threat response as an example.
  • ServiceNow describes multi-agent orchestration across IT, security, APM, network management, and human approval.
  • Atlassian describes human-in-the-loop software agents that plan, code, validate, and raise PRs.
  • McKinsey maps agentic AI to KYC, transaction monitoring, sanctions, and fraud investigations.
  • IBM frames agentic data management around dynamic workflow validation, lineage, semantic validation, and guardrails.
  • IBM describes regulated back-office automation across Office artifacts, SQL, approvals, audit trails, and rollback.