Creating a Governed Autonomous Agent in Quantin

An autonomous agent should be defined by a responsibility and a boundary, not by unlimited access to tools. Quantin agents combine observations, reasoning, memory, and actions under explicit policy and human authority.

Define one responsibility

Write a purpose statement that includes the agent’s user, intended outcome, and exclusions. “Prepare a daily market-risk briefing for the portfolio team” is testable. “Handle investment decisions” is too broad for a first deployment.

Assign knowledge and tools

Connect only sources relevant to the responsibility. Grant read-only tools first. For every action tool, document allowed operations, target scope, rate limits, and approval requirements.

Create the policy boundary

• Data the agent may access.
• Actions it may propose or execute.
• Financial, operational, and frequency limits.
• Conditions that require human review.
• Conditions that immediately stop a run.

Build an evaluation set

Use representative normal cases, ambiguous requests, missing-data cases, policy conflicts, and adversarial inputs. Score task quality, evidence use, policy compliance, escalation behavior, and action accuracy separately.

Deploy in stages

1. Offline: evaluate against fixed cases.
2. Shadow: observe live inputs without affecting production.
3. Assistive: propose actions for human approval.
4. Bounded autonomy: execute low-risk actions inside deterministic limits.

Monitor outcomes

Track completion quality, evidence coverage, overrides, policy blocks, tool errors, cost, and downstream outcomes. Review unusual behavior and update policies before expanding scope.

Autonomy is earned by evidence. A governed agent becomes more useful as its purpose, permissions, and observed performance become clearer—not simply as more tools are added.