By Tony Wood, AXD Institute · Published 2026-03-01
What is Multi-Agent Treasury Operations | AXD Institute?
Multi-Agent Treasury Operations — an AXD Institute resource on agentic experience design, agentic commerce, trust architecture, and human agent interaction. Founded by Tony Wood..
How does AXD differ from traditional UX?
Why is trust architecture important for agentic AI?
Key concepts in Multi-Agent Treasury Operations | AXD Institute
Agentic Experience Design (AXD)
Trust architecture for autonomous AI
Delegation design patterns
Human agent interaction models
Agentic commerce and machine customers
Agency requires intentional delegation — every agentic system begins with a designed act of delegation
Trust is the primary material — AXD works in trust rather than attention
Absence is the primary use state — the most consequential experiences happen when no one is watching
Relationships have temporality — agentic experiences accumulate history over time
Outcomes replace outputs — AXD designers specify results, not interfaces
Dimension
Traditional UX
Agentic Experience Design (AXD)
Primary material
Attention and affordance
Trust and delegation
User state
Present, navigating
Absent, delegating
Design output
Screens and interfaces
Outcomes and constraints
Temporal model
Session-based
Relationship-based
Success metric
Task completion
Trust calibration
Frequently Asked Questions
How does AXD handle multi-agent systems?
AXD handles multi-agent systems through orchestration visibility (rendering multi-agent activity as a single coherent narrative), delegation chains with explicit priority ordering, agent-to-agent negotiation protocols, and cascading failure containment.
What is orchestration visibility?
Orchestration visibility is the design of how multiple agents' activities are rendered as a single coherent narrative for the human principal, replacing fragmented per-agent dashboards with a unified story of collective autonomous action.
Key Takeaways
A commercial bank designed an agentic treasury management system where multiple AI agents operated simultaneously: a cash positioning agent optimising liquidity across accounts, a payments agent managing supplier settlements, a forex agent hedging currency exposures, and a compliance agent monitoring regulatory thresholds. The design challenge was not individual agent capability but orchestration: when four agents with overlapping authority operate on the same pool of corporate funds, who has priority? How does the CFO observe what is happening? How do agents negotiate conflicting objectives - the cash agent wants to concentrate funds while the payments agent needs to distribute them?