Point C1
AI Delegation Orchestration: A Series on Durable Agent Work
This package is a seven-part article series about a shift in AI work design:
Natural language can remain the human interface, but consequential AI work needs durable delegations, explicit records, operator control surfaces, and domain-specific review boundaries.
The series is intentionally technical, but it is not only for developers. Coding is the easiest place to see the problem because code has tests, diffs, branches, pull requests, and rollback. The same pattern appears in research, legal review, education, policy, finance, government, and business operations once AI systems begin doing multi-step work with evidence, state, risk, and handoff.
Reading Path
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From Conversation to Delegation
Why chat and voice can remain the interface while delegation becomes the durable work primitive. -
The Delegation Record
A proposed record schema for objective, scope, non-goals, control boundaries, evidence, freshness, review, rollback, and exit conditions. -
The Operator Cockpit Problem
Why traces, summaries, and dashboards are insufficient without next-best-control across active delegations. -
Control Loci, Not Human Managers
An agent-native routing model: executor, verifier, arbiter, policy, context refresh, and human/principal review. -
Long-Running Delegations
How agents can continue for hours without repeatedly interrupting humans, while preserving checkpoints and stop conditions. -
Capability Contracts for Agent Networks
Why agent systems need replaceable capabilities with explicit contracts, not agent job titles. -
Commitment Boundaries in High-Stakes Domains
Why high-stakes AI use is not binary, and how evidence, review, appeal, privacy, and accountability change by domain.
Sources
- Malone and Crowston, The Interdisciplinary Study of Coordination. https://crowston.syr.edu/sites/default/files/acmcs94.pdf
- Endsley, Toward a Theory of Situation Awareness in Dynamic Systems. https://journals.sagepub.com/doi/10.1518/001872095779049543
- OpenAI Agents guide. https://developers.openai.com/api/docs/guides/agents
- LangGraph documentation. https://docs.langchain.com/oss/python/langgraph/overview
- Model Context Protocol introduction. https://modelcontextprotocol.io/docs/getting-started/intro
- NIST AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework
Publication Note
This series 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.
Sources Sources used 6 sources Show sources Hide sources
- Malone and Crowston, The Interdisciplinary Study of Coordination paper
- Endsley, Toward a Theory of Situation Awareness in Dynamic Systems paper
- OpenAI Agents guide documentation
- LangGraph documentation documentation
- Model Context Protocol introduction protocol
- NIST AI Risk Management Framework report
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Sources and notes
These notes collect the sources, counterpoints, and review status behind the article's important points. Read the essay first; open this when you want to check something.
Confidence reflects how strongly the sources support the point (low / medium / high). Status describes the point's role (e.g., core, argument, landscape). Sources link to supporting material; counterpoints note boundary conditions or conflicting findings.
AI interfaces can stay conversational, but consequential AI work should be governed through durable delegations, explicit records, operator control surfaces, and domain-specific review boundaries rather than chat transcripts alone.
verified reviewed 2026-06-28
- Sources (5)
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“Coordination theory frames complex work as dependencies among activities, which supports treating agent work as coordination design.”
Malone and Crowston, The Interdisciplinary Study of Coordination background -
“Situation awareness research supports separating live interface cues from the durable state needed to understand dynamic work.”
Endsley, Toward a Theory of Situation Awareness in Dynamic Systems analogous -
“Current agent tooling exposes tools, handoffs, tracing, and human review as explicit orchestration concerns.”
OpenAI Agents guide indirect -
“Current graph-based agent frameworks emphasize stateful, controllable execution rather than a single linear transcript.”
LangGraph documentation indirect -
“Risk-management guidance supports explicit governance, measurement, and accountability around AI system behavior.”
NIST AI Risk Management Framework background
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- Counterpoints (1)
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The delegation record and next-best-control models are proposed design primitives, not accepted standards.
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Review recordHow this was madeShow detailsHide details
Created 2026-06-28 by codex-agent.
Policy: policy:default v1.0.0.
✓ Approved hash matches current article
Agent runs
- drafting-and-site-previewgpt-52026-06-28
in:1fb34c15…out:1adcd6cf…
Reviews
- sibling-agentcommented2026-06-28
Scope: series structure, evidence anchoring, privacy
Earlier sibling review identified source-ledger gaps; those gaps were resolved before publication with article-specific source alignment.
- humanapproved2026-06-28
Scope: publication, reader experience, privacy, series structure
contentHash:
31e452aa5be128c8…Human maintainer approved the local preview for website publication after article layout, reading-flow, privacy, and series-structure review.
- sibling-agentapproved2026-06-28
Scope: publication-gate, source alignment, provenance, privacy, generated artifacts
contentHash:
31e452aa5be128c8…Independent sibling review approved the final publication packet with no blocking findings after source-ledger, provenance, privacy, and generated-artifact checks.
Machine-readable files
The same points, sources, and relationships are also available as structured files for agents and tools. The JSON follows the publication record schema.