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Panaptico is not fully autonomous and not fully manual. It uses governed execution — bounded, permissioned, auditable work where humans retain control at every decision point while AI handles discovery, sequencing, and routine work.

How tasks work

Every task in the implementation checklist follows a governed lifecycle:
1

Task is created with requirements

Each task has acceptance criteria, evidence requirements, dependencies, an owner, and optionally an approval gate
2

Dependencies are checked

A task cannot start until its upstream dependencies are completed. Blocked tasks are visible in the dependency graph.
3

Work is executed

The owner works through the task — manually, with AI assistance, or through bounded automated execution in a sandboxed environment
4

Evidence is attached

Completion requires proof: files, screenshots, test results, configuration exports, links, or verification output. Tasks cannot be marked done without evidence.
5

Approval is routed

If the task has an approval gate, it routes to the named approver. The approver can approve, request changes, or escalate.
6

State is recorded

Every state change — pending → in progress → done, approval granted, evidence attached, risk escalated — is timestamped and attributed in the audit trail.

Evidence model

Evidence is not optional documentation. It is structured proof linked to specific tasks: Tasks can define what evidence is required before they can be completed.

Approval chains

Approvals in Panaptico are explicit:
  • Each approval gate has a named approver — not “someone from the team”
  • Approvers see the task context, evidence, and execution results
  • Three outcomes: approved, changes requested, or escalated
  • Approval history is recorded in the audit trail
  • Unresolved approvals are surfaced in the project overview as risks

AI-assisted execution

For tasks that benefit from automation, Panaptico provides AI-assisted execution:
  • Task-scoped agents can generate diagnostics, remediation plans, and configuration files
  • Execution runs in sandboxed environments with explicit dependency handling
  • Output is captured as evidence and linked to the task
  • Agents cannot bypass approval gates or modify blueprint-wide state without explicit request
The AI assists — it does not override human judgment.

Risk and blocker management

When work is blocked:
  • Tasks can be marked as blocked with a reason and downstream impact
  • Risks are tracked with severity, ownership, and resolution status
  • Blocking risks surface in the project overview intelligence dashboard
  • The dependency graph shows which downstream tasks are affected
  • Risk resolution is recorded with evidence

What makes this different from a ticket system

Next steps

Post-implementation

What happens after go-live

Executing tasks

Step-by-step task execution guide