A mandate with conditions
The unit of review is a delegation in a workflow: a task, population, configuration, owner and period of use. Apply it to agents, copilots and consequential automation. A label does not determine the need for oversight.
Authority is specific
Distinguish reading, recommending, drafting, submitting, changing and delegating. Record when each is permitted and when a person must approve. Capability does not confer permission.
Stable identity, configuration, individual execution, credential and delegated authority are separate records. Rotating a credential does not create a new agent.
Consequences are contextual
A booking can be restored after a time-sensitive opportunity has passed. A draft can influence a decision before it is corrected. Assess harm, timing, scale and recovery, including effects mediated through people.
Approval requires a reviewer with context, time, competence and authority. Credential revocation is one containment control, not proof that all activity has stopped.
The Trusted AdvIsor
A lens on the people, institution and working practices behind AI. Confidence should be proportionate to evidence for a defined use.
Trustworthiness =Credibility + Reliability + IntimacySelf-orientation
From Maister, Green and Galford, The Trusted Advisor (2000). Their associated materials emphasise the human basis of intimacy and self-orientation. Original framework.
No trust score. This OPL adaptation uses the dimensions as questions. It does not measure a model’s intentions or allow strengths to cancel serious failures. No endorsement by the original authors is implied.
Three cases that resist easy answers
Fictional design exercises. These are neither deployment recommendations nor results from real services. Explore the hidden consequence before carrying a case into the record.
Who gains time and who does extra work?
A narrow workload calculation makes assumptions visible. It cannot establish safety, access, equity, financial return or overall benefit. All default numbers are illustrative.
Enter incremental minutes attributable to this delegation, using the same case population. Avoid double counting review, correction and coordination. Staff capacity released is not automatically a cash saving.
Keep other outcomes visible. Compare resolution, repeat contacts, delay, serious errors, patient effort, access to human support and subgroup effects. Agree unacceptable outcomes before a pilot. A positive time balance does not offset a safety failure.
The delegation record
Describe the work and record the evidence behind it. The guide checks completeness, not the truth or adequacy of your evidence. A human decision is recorded separately.
Local working copy. Entries stay in this page until you download a record. Reloading discards unsaved changes. There is no automatic saving or transmission. Export only information appropriate to the record’s audience.
1 · Purpose and accountability
2 · Authority and operating boundaries
3 · Evidence profile
Unknown → declared → evidenced → tested describes the state you report, not a maturity score. For evidenced or tested claims, include a reference, reviewer, date and scope or finding. “Not applicable” requires justification. Keep concerns visible.
4 · Human decision and conditions
Recording a decision here does not confer authority. Use your organisation’s applicable approval, clinical safety, privacy and risk processes. A bounded pilot may investigate benefit; expansion needs stronger evidence.
5 · Inspect the record to export
From analogy to evidence
This is an AI-assisted conceptual synthesis, revised through red-team review. It has not been validated through a practitioner study. Agreement among model outputs is not independent expert consensus.
Keep useful biology
Anatomy reveals dependencies; ecology reveals interactions and resource burdens. We have dropped the taxonomic tree. In biological usage, parasitism is a type of symbiosis; mutualism describes mutual benefit.
Here, beneficial coexistence concerns the people and institutions using AI. It does not assume that the AI has welfare interests. Ask whose work improves, whose burden increases and who bears risk.
Evaluate the guide itself
Compare decisions made with existing language and with this guide. Use comparable cases, counterbalance order, and include staff and people affected. Measure missed hazards, false reassurance, responsibility clarity, time and agreement on next steps.
Check whether the metaphors add value beyond the record. Preserve contrary findings. A successful small exercise supports usability, not universal validity.
What changed from v2
Delegated work replaces taxonomy. Purpose and benefit join authority and accountability. Identity is separate from credentials. Action-specific consequences replace automatic autonomy ceilings. Evidence states and unknowns replace deployment verdicts. The Trusted Advisor adds questions about warranted reliance. Earlier versions remain intact.
Sources and companion paper
The companion v3 paper is supplied separately and covers the argument, examples, limitations and operating cycle. Sources checked 19 October 2026; inclusion does not imply endorsement. The original shared chat was unavailable; supplied local papers and Trusted Advisor reflections informed the revision.
[1] Figlioli et al. · Healthcare’s AI agent management challenge — practitioner hook.
[2–3] Trusted Advisor Associates · Trust Equation and core concepts — attribution and limits of the analogy.
[4] Anthropic · Building effective agents — architecture and workflow patterns.
[5] Feng et al. · Levels of Autonomy for AI Agents — proposed interaction framework.
[6] IETF · Token revocation — containment constraints.
[7] HHS · Minimum necessary requirement — scope and exceptions.
[8] NHS · Make your service clinically safe — applicable safety processes, including DCB0129 and DCB0160.
[9] OpenStax · Relationships and microbiomes — biological terminology.
[10] NIST · AI RMF Playbook — established risk-management functions.