Agentic Decision Systems — from assist to autonomous execution

Enterprise agents succeed when treated as decision systems with world models, plans, verifiers, repair loops, and auditable traces. Autonomy scales up a ladder—Assist → Approve → Auto-with-review → Auto—bounded by gates on accuracy, oversight minutes, escalation rate, unit cost, and incidents avoided. Production deployments at Klarna, Intercom, Walmart, LILT, and others show "Auto-with-review" is commercially viable for bounded workloads, while compliance-heavy flows remain at "Approve."

• World model: structured state over entities, policies, constraints, and affordances.
• Planner: produces minimal, constraint-satisfying plans (graph of tool calls/sub-goals).
• Actor: executes schema-checked tool calls or controlled computer-use with rate limits.
• Verifier: checks pre/post conditions; supports LTL/model checking for compliance.
• Memory: episodic traces with drift detection and freshness metadata.
• Loop: plan → act → verify → repair → resume with escalation paths and rollback gates.

1. Goal + constraints intake with policy + PII tagging.
2. Plan compiled with LTL checks and static analysis.
3. Execute actions via tool APIs or controlled computer-use.
4. Verify post-conditions; run read-after-write checks.
5. Repair or escalate via exception channels when checks fail.
6. Commit trace to audit store; sample reviews feed evaluation buffers.

For inAi product work, this remains a design pattern rather than a public performance claim: catalog workflows, career workflows, and agent-facing tools should use review gates, audit artifacts, drift checks, and rollback paths before any autonomy is increased. Thresholds belong in scoped evaluations, not in broad public product promises.