Cross-boundary decisions
Agent Negotiation
In real enterprise operations, no single person or system controls everything. A demand planner can't unilaterally increase production, that's the plant manager's domain. A procurement analyst can't redirect logistics, that's the logistics team's call. Meaningful responses across the six decision domains cross multiple authority boundaries.
Traditional systems handle this with emails, meetings, and phone calls. A demand spike triggers a week of cross-functional coordination before anyone acts. By then, the opportunity, or the crisis, has moved on.
Autonomy solves this with Agent-to-Agent authorization, autonomous agents negotiate cross-boundary decisions in seconds, under the AIIO operating model with full transparency and human override at every step.
How Agent Negotiation Works
From request to cross-functional resolution in seconds
A Real Example: The Rush Order
From natural language request to cross-functional execution in 15 seconds
"Bigmart just called, they need 500 C900 bikes delivered to Detroit in 2 weeks. This is a new fleet deal we can't lose."
The system recognizes this as both a demand signal (a new customer order) and an implicit directive (do whatever it takes to fulfill it). It creates the order and checks feasibility.
The AI strategist generates three candidate resolution strategies:
Strategy C has three actions. The demand planner's authority is checked for each:
Cross-boundary actions are sent to the owning agents for evaluation. Each agent checks its current domain state and responds:
The entire decision, all three strategies evaluated, the winner selected, the agent-to-agent conversation, the counter-offer from Procurement, is recorded as a single decision in the Decision Stream. Any stakeholder can Inspect the reasoning or Override any action.
The AIIO Decision Model
Every decision in Autonomy follows the four AIIO states. This is the governance model that makes autonomous operation safe, agents decide first (speed), but humans always have the final say (governance).
System auto-selects the best strategy and executes within-authority actions. No human delay for routine decisions.
Decision surfaced to the Decision Stream with full reasoning, strategy comparison, and agent-to-agent conversation.
Planner reviews the comparison table, authority boundaries crossed, and agent reasoning. Full audit trail.
Human selects a different strategy or rejects an action. The override reason feeds back into agent training, teaching agents which overrides improve outcomes.
This mirrors Kahneman's insight about fast and slow thinking applied to organizations. The agents are System 1, fast, pattern-matched, handling routine decisions automatically. Human planners are System 2, slow, deliberate, activated when something looks wrong. The AIIO model ensures System 2 always has visibility and veto power, without requiring it to process every decision.
Authority Boundaries
Every agent has three categories of actions. These boundaries mirror how real organizations work, a demand planner can adjust priorities (their domain), but requesting a production increase crosses into the plant manager's domain and requires authorization.
15 agent roles span the enterprise: Sales/ATP, Supply, Allocation, Logistics, Inventory, Plant, Quality, Maintenance, Procurement, Supplier, Channel, Demand, Finance, Service, and Risk. The authority boundary map is exhaustive, every action type is mapped to exactly one domain owner. Unknown actions default to requires-authorization (pessimistic safety).
How Agents Respond
When an agent receives an authorization request, it evaluates against its current domain state, inventory levels, capacity utilization, active commitments, and responds:
Feasible, no contention. Action executed as-is.
Feasible with modifications. "I can do 60 units instead of 80 without overtime."
Infeasible or constraint violation. Action skipped, reason recorded.
Agent uncertain. Pushed to Decision Stream for human Inspection.
Counter-offers are the most common outcome, agents rarely say "no" outright. Instead they negotiate: "I can't do 80 units at standard cost, but I can do 60 without overtime." This mirrors how real cross-functional teams work, but at machine speed.
The Agentic Operating Model
This is what the agentic inversion looks like in practice. Agents own decisions by default. Humans Override with reasoning captured. The more decisions flow through the system, the better the agents become, and the less human effort is required for routine coordination.
The judgment layer, knowing when to Override and why, becomes the organization's competitive advantage. Override reasons feed back into the learning flywheel, teaching agents which human judgments consistently improve outcomes. Over time, the agents internalize the organization's decision culture.
See Agent Negotiation Live
Watch agents resolve a cross-functional crisis in real-time.