What to do: Step 1 — Set the authority level for each of the nine digital workers in the AI Project Authority Matrix (Observe, Recommend, Prepare, Execute or Autonomous). Step 2 — Review the Project Digital Twins and AI FinOps economics. Step 3 — Write your agentic operating doctrine (minimum 80 characters) defining what agents may execute, where human approval remains mandatory and how escalation works. Click Seal authority model .
AI AGENTS 9 Digital delivery team
AUTONOMY INDEX 39% Configured authority
AUTHORITY EXPOSURE 37 Higher is more exposed
AGENT SCORE 64 Command profile
Project Control Agent Moves plans and identifies critical-path conflicts.
Schedule, milestones, dependencies Observe Recommend Prepare Execute Autonomous Risk Agent Scans telemetry and predicts material delivery exposure.
Risk sensing, thresholds, escalation Observe Recommend Prepare Execute Autonomous Business Analyst Agent Challenges scope and preserves requirement-to-value lineage.
Requirements, traceability, outcomes Observe Recommend Prepare Execute Autonomous Technical Agent Interprets technical debt, design disputes and delivery implications.
Architecture, engineering translation Observe Recommend Prepare Execute Autonomous Finance Agent Tracks portfolio spend, inference cost and value realization.
Burn, forecast, unit economics Observe Recommend Prepare Execute Autonomous Governance Agent Maintains gates, exceptions and approval evidence.
CISO, Legal, Compliance, model risk Observe Recommend Prepare Execute Autonomous Vendor Agent Surfaces model changes, commercial risk and exit exposure.
Contracts, SLAs, dependencies Observe Recommend Prepare Execute Autonomous Change Agent Tracks whether delivered capability becomes operational value.
Adoption, workforce readiness, sentiment Observe Recommend Prepare Execute Autonomous Evidence Agent Preserves what was authorized, by whom and on what evidence.
Decision record, audit trail, evidence wallet Observe Recommend Prepare Execute Autonomous PROJECT DIGITAL TWINS
The future is a probability, not an order. Fraud ML 71% probability of material delay Validation queue + shared ML engineer
VALUE AT STAKE · $9.0M Customer Chatbot 43% probability of material delay Red-team gate + vendor model change
VALUE AT STAKE · $5.7M AML Optimization 68% probability of material delay Feature-store dependency + lineage
VALUE AT STAKE · $12.4M Agentic Operations 55% probability of material delay CISO slot + tool authority design
VALUE AT STAKE · $7.1M AI FINOPS · LIVE ECONOMICS $5.1M forecast on a $3.8M run-cost budget. Frontier-model routing accounts for 47% of spend. Repeated agent loops are now a budget risk, not just an engineering detail.
Frontier model 47% Mid-tier model 31% Small model 12% Embedding + other 10%
YOUR AGENTIC OPERATING DOCTRINE Delegation must survive the audit trail.