← Glossary
Progressive autonomy
Progressive autonomy is how you make agentic systems safe to adopt: you don’t jump from “suggestions” to “unmanned automation”. You climb a ladder where each step earns trust through evidence, guardrails, and reversible actions.
Definition
- Progressive autonomy is a staged increase of agent authority over time: suggest → draft → act with confirmation → act with notification → act autonomously within strict bounds.
- Each step has clear acceptance criteria: error rates, recovery performance, user trust signals, and operational readiness.
- It is a governance pattern as much as an interaction pattern.
- It’s the opposite of “move fast and break things”: autonomy grows only when guardrails, auditability, and recovery are proven in production.
Why it matters
- Teams underestimate the last 10%: safe autonomy is mostly about edge cases, escalation, and operator tooling.
- Progressive autonomy reduces risk while accelerating adoption: users learn the system, and the system learns the context.
- In regulated environments, progressive autonomy provides an audit-friendly path to capability.
- In practice, this is where many digital programs fail: the concept is understood, but the operating discipline is missing.
Common failure modes
- All-or-nothing autonomy decisions driven by hype or executive pressure.
- No acceptance criteria: autonomy increases because it “feels fine”.
- Confirmation fatigue: asking for approval on everything, so users blindly click through.
- Autonomy without recovery: when wrong, the agent can’t undo or hand off.
- No operator readiness: support and compliance are surprised by automation.
How I design it
- Define the autonomy ladder per job-to-be-done and per risk level. Not everything deserves autonomy.
- Attach acceptance criteria to each level: accuracy, time-to-recovery, dispute rate, support load, policy violations.
- Design handoff and override flows: interruption, manual takeover, rollback, and dispute.
- Instrument outcomes and near-misses. Treat guardrail catches as learning data, not noise.
- Communicate autonomy clearly: users should always know “who acted” and “what authority was used”.
- Publish autonomy levels in the UI and in internal docs so everyone can reason about risk with the same vocabulary.
- Treat it as a repeatable pattern: define it, test it in production, measure it, and evolve it with evidence.
Related work
Proof map claims
Case studies
See also
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