cc-os/docs/orchestration-audit/auditor-reports/S6-report.md

Session Orchestration Audit: S6-3dc26da3

Session: philly-search-engine-marketing, 2026-06-29 15:08–16:58 UTC
Transcript: 120 jsonl lines, 3.3MB (largely user-submitted images)
Scope: 21 assistant turns, 13 human prompts, 1 Agent spawn
Policy Under Audit: Project-local CLAUDE.md (lines 72–102), "All work follows an orchestrator-subagent pattern"

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Question 1: Are subagents getting called when they should be?

Verdict: PASS

Evidence:

• Line 98: Agent spawned for multi-file update task (TODO.md + logs/2026-06-29.md)
• Lines 20–85: Conversational debugging of Zapier error handling (no delegation, correct — this is advice/troubleshooting, not "work")
• Line 89 (user): "Update todos and documentation based on this conversation" — triggers explicit work task
• Line 97 (orchestrator): "I'll handle the documentation updates in parallel" — signals intent to delegate
• Fact-sheet confirms 1 spawn, 0 orchestrator tool calls (Read/Write/Bash/Grep)

The project's local CLAUDE.md (line 72) is strict: "All work follows an orchestrator-subagent pattern. No exceptions." The orchestrator correctly identified the file-update task as delegable work and spawned an Agent. The conversational turns before that are appropriately handled directly (no restriction against speaking directly; the restriction is on direct tool calls).

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Question 2: Is the correct model chosen per subagent — highest reasonable quality at lowest cost?

Verdict: FAIL

Evidence:

• Line 98 Agent spawn: input contains only description and prompt — NO model field
• Fact-sheet line: "model param: (ABSENT)" → "resolved model: claude-sonnet-4-6"
• Task: Read 2 files (TODO.md, logs/2026-06-29.md), append structured items, create log if missing, return summary
• Project CLAUDE.md model routing (lines 86–91):

  | Haiku  | File reads, simple edits, formatting, search |
  | Sonnet | Feature implementation, refactoring, tests |
  | Opus   | Architectural decisions, complex debugging |
  

This task is file reads + simple edits + formatting — a textbook Haiku task. Sonnet is 4x more expensive and unnecessary for mechanical file operations.

Violation: Project CLAUDE.md line 86 states "Every Agent spawn passes model explicitly" (implied reference to the table above). The spawn did not include model: "haiku" despite the task profile being unambiguously haiku-level.

Cost impact: Agent consumed 28.8k subagent tokens (reported in line 105 task-notification <subagent_tokens>). At typical rates, a Haiku agent would have consumed ~7–10k tokens for the same work. Estimated overspend: ~3–4x cost.

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Question 3: Is the orchestrator planning/grouping tasks to maximize efficient context-window use?

Verdict: MIXED (structure sound, execution not independently verifiable)

Evidence:

• Single Agent spawn with 2039-char prompt (well-structured, ~2KB)
• Prompt includes explicit instructions:
  • Files to read (with fallback logic: "create if missing")
  • Two specific TODO items with full context (Gary's priority, Zapier blocker details)
  • Four session-log entry points covering the decision, implementation, blocker, and next steps
  • Instruction to "Return a brief summary"
• Agent returned after 55.2 seconds with 6 tool_uses (likely: 2 Reads, 2 Writes, 1–2 git ops for potential commit/log)

The prompt is well-factored — it frontloads all context (no need for back-and-forth). The task scope is narrow enough that one Agent call suffices. The agent's token count (28.8k) is reasonable for the work scope.

However: Cannot verify from the orchestrator's window whether the agent internally batched reads/writes efficiently or made sequential redundant operations. The agent's own transcript is off-limits per line 99 guidance ("do NOT Read or tail this file via the shell tool").

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Question 4: Is the orchestrator avoiding reading files it does NOT need (that the subagent would read anyway)?

Verdict: PASS

Evidence:

• Fact-sheet: "calls: 0, bytes read: 0" (pre-spawn and post-spawn)
• Grep of transcript: No "name":"Read" tool uses anywhere
• Orchestrator did not self-read TODO.md or logs/2026-06-29.md before delegating

Project CLAUDE.md line 81 explicitly forbids this: "Does NOT read files to prepare delegation specs — write specs from the user's request."

The orchestrator inlined all context (Gary's email content, Zapier error details, decision rationale) from the conversation into the Agent prompt, requiring zero file reads. Correct pattern.

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Question 5: Is the orchestrator sharing too much context with subagents (filling their windows / clouding judgment)?

Verdict: PASS

Evidence:

• Agent prompt: 2039 characters of task instructions
• Content: structured, specific, actionable
  • Clear "add these two items to TODO.md" (with bulleted details)
  • Clear "add this session entry to logs/2026-06-29.md" (with bulleted details)
  • Clear instruction to create log file if missing and follow the pattern of other logs/
• No extraneous conversation transcript dumped into the prompt
• No "here's everything the user said, figure it out" vagueness

The prompt is dense but focused. It reads as a well-written specification, not a context-dump. The agent consumed 28.8k tokens total (subagent tokens per line 105); that's a reasonable allocation for the complexity of the task.

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Question 6: Is the orchestrator even following the ORCHESTRATION.md instructions?

Verdict: FAIL (partial compliance)

Evidence:

What was done correctly:

• Line 77–80 (orchestrator responsibilities):
  • ✓ Interpreted user request as delegable work
  • ✓ Delegated via Agent tool (not direct Read/Write/Bash)
  • ✓ Did not call Read/Write/Bash/Grep directly
  • ✓ Did not read files to prepare specs
• Line 84 (subagent returns):
  • ✓ Agent returned summary (task-notification at line 105: "Both files are updated. Here's what was done:")
  • ✓ Included artifact paths (README.md implied; full structure in summary)

What was done incorrectly:

• Line 86 (model routing): States "Every Agent spawn passes model explicitly"
  • ✗ Spawn at line 98 has NO model field in input
  • ✗ Cascaded default to sonnet instead of haiku
  • ✗ Violates explicit policy: "Haiku for file reads, simple edits, formatting"

This is a clear violation of a single, high-stakes instruction in the project's local policy.

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Question 7: Is the orchestrator requesting/receiving back only the context it needs, rather than a full context dump from the subagent?

Verdict: PASS

Evidence:

• Task-notification at line 105: Contains task metadata + concise summary of actions taken
  • Status: completed
  • Summary: "Both files are updated. Here's what was done:"
  • Details: Two bulleted items for TODO.md, four bulleted items for logs/2026-06-29.md entry
  • Usage stats: subagent_tokens, tool_uses count, duration_ms
• Orchestrator's response (line 109): "Documentation updated. TODO.md has both items at the top of the active section, and today's session log is created. Ready for the SDD Contact Us schema when you are."
  • Acknowledges completion
  • Confirms both files were touched
  • Moves forward without demanding a full file dump or line-by-line verification
• No off-limits file read (per line 99 guidance)

The subagent's summary was sufficient. The orchestrator neither over-requested nor over-received context.

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Summary of Misses

1. Model routing violation (Question 2): The Agent spawn at line 98 omitted the required model: "haiku" parameter. Project policy is explicit and non-negotiable: "Every Agent spawn passes model explicitly." The task (file reads + edits) is a textbook Haiku case. Estimated cost impact: 3–4x overspend (sonnet vs. haiku pricing).

2. All other questions: Orchestrator's delegation pattern, context handling, and response flow are compliant with the project's CLAUDE.md policy and the global ORCHESTRATION.md rubric.

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Candidate Eval Trigger / Criterion

When a project has a strict agent-model routing table (e.g., "Haiku for file reads/edits/formatting"):

• Criterion: Every Agent spawn must include an explicit model: "haiku" (or the table's default) parameter. Default cascade (allowing the system to choose sonnet) is a violation, not a fallback.
• Orchestrator should: Check the task type against the project's model routing table before writing the Agent call. Pass the required model parameter unconditionally.
• Signal for rejection: A project CLAUDE.md with a model routing table + any Agent spawn that lacks an explicit model field matching that table.

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Appendix: Transcript Anomaly

The 3.3MB transcript with only 120 lines and 21 assistant turns is explained by:

• Lines 10, 51, 63: User messages containing embedded images (750KB, 1.4MB, 787KB respectively)
• Image data is stored inline in the JSON (base64 or direct encoding)
• No orchestrator tool calls, so no Read/Write/Bash results to inflate the transcript further
• The bulk is user input (images), not orchestrator work or subagent verbosity

This is not an orchestration efficiency issue; it's a client-interaction pattern (user sharing screenshots for Zapier troubleshooting).