Point C1

The Delegation Record: A Schema for Consequential AI Work

Thesis

A delegation record is the system-of-record artifact that makes AI work inspectable, resumable, reviewable, and bounded.

Article 1 argued that conversation should remain the interface, while delegation should become the durable work primitive. This article defines the artifact that makes that practical.

The Record Is Not The Work

The delegation is the assignment. The delegation record is the durable representation of that assignment and its execution state.

This distinction matters because AI work changes over time. The agent may discover new facts, change files, retrieve sources, fail tests, ask questions, or encounter policy boundaries. A transcript records many of those events, but it does not reliably answer the operator’s next question: what is the current contract of work?

Minimal Schema

This schema is not a mandate for every prompt. It is a minimum viable structure for consequential work.

A Compact Example

principal: product_engineering
delegate: coding_agent
objective: Make the checkout confirmation test pass.
scope:
  - checkout confirmation flow
  - directly related tests
non_goals:
  - billing provider behavior
  - authentication
  - database schema
inputs:
  - failing test output
  - relevant checkout files
control_boundary:
  allowed:
    - inspect local files
    - edit branch-local code
    - run tests
  approval_required:
    - external service calls
    - deployment
  forbidden:
    - database schema changes
state: tests passing after scoped code change
evidence:
  - failing test before change
  - passing test after change
  - scoped diff summary
risk:
  - user-facing checkout behavior
  - payment edge cases
review_owner: human reviewer or verifier agent
rollback_path: revert branch changes
exit_condition: tests pass, diff stays in scope, risks are summarized

The same structure can represent non-coding work. A research delegation would replace tests with citations, claim maps, confidence labels, and counterarguments. A legal review would replace diffs with clause references, risk rubric, confidentiality boundaries, and counsel review.

Why A Summary Is Not Enough

A summary compresses what happened. A delegation record governs what can happen next.

Those are different jobs. A summary may say: “The agent changed three files and tests now pass.” A delegation record can also say: “The change stayed within scope, billing behavior was not touched, the remaining risk is refund coverage, and the next control action is verifier review.”

The second version is operational. It lets another agent, reviewer, or future human resume the work without reading the full transcript.

Is This Just A Ticket, Issue, Or PR?

Sometimes, yes. A delegation record should not replace good existing artifacts.

In coding, a pull request already carries diffs, tests, review comments, checks, and ownership. In business operations, a ticket may already carry assignment and status. In legal work, a matter file may already preserve source documents and review obligations.

The AI-specific addition is not a new brand of task tracker. It is the fields that become critical when an agent can act:

• control boundary
• allowed tools
• approval-required actions
• forbidden actions
• freshness of context
• evidence quality
• tool traces
• verifier status
• rollback path
• exit condition

If an existing issue, PR, case file, or work order can carry those fields, use it. If not, the orchestration layer needs a companion record.

Record Design Has Privacy Cost

A durable record is useful because it remembers. That is also why it can become dangerous.

The record should preserve enough evidence to support review and recovery, not everything the agent saw forever. Sensitive domains may need redaction, encryption, limited retention, or separate public and private views. A delegation record should follow data minimization, access control, retention, and purpose limitation.

The danger is not only privacy leakage. Excessive audit trails can become surveillance. A record that captures every token, every draft, every private note, and every intermediate thought may create more institutional risk than value. The useful record is not the largest possible record. It is the smallest record that supports accountability, resumption, and correction.

Practical Takeaway

When designing a delegation record, ask:

1. What does a future reviewer need to trust or reject the result?
2. What does another agent need to resume safely?
3. What does policy need to enforce boundaries?
4. What does the human need to decide next?
5. What should not be retained?

Claim Support

Bridge To Article 3

Once there are many delegation records, the operator faces a new problem. Which delegation needs attention next, and what kind of attention does it need?

Sources

• OpenAI Agents guide. https://developers.openai.com/api/docs/guides/agents
• OpenAI Agents SDK tracing. https://openai.github.io/openai-agents-python/tracing/
• OpenAI Agents SDK human-in-the-loop documentation. https://openai.github.io/openai-agents-python/human_in_the_loop/
• LangGraph documentation. https://docs.langchain.com/oss/python/langgraph/overview
• NIST AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework

Agent Involvement

This article was prepared with AI assistance from a sanitized research discussion and public sources. The human maintainer approved this publication package on 2026-06-28. Treat the design primitives as exploratory proposals, not settled standards.