enhancement: deterministic blocked-by resolution via cached dependency graph

[robstiles]

Description

The pulse system currently relies on the LLM supervisor to transition status:blocked → status:available when blockers resolve. The LLM supervisor is gated behind _should_run_llm_supervisor() which requires either a 1-hour backlog stall (PULSE_LLM_STALL_THRESHOLD=3600) or a 24-hour daily sweep. This creates a systemic delay in dependency chains: when a worker completes a task and closes its issue, downstream tasks that were blocked by it remain status:blocked for up to 1 hour — even though the resolution check is entirely deterministic.

Observed impact

A managed private repo has a 15-task dependency chain (sequential phases where each task is blocked-by its predecessor). When the pulse dispatches workers for the first available tasks and they complete successfully (PRs merged, issues closed), the downstream tasks should become available for dispatch immediately. Instead:

1. Workers complete 3 tasks, closing issues and merging PRs (~20 min total)
2. _should_run_llm_supervisor() sees the backlog decreased (3 fewer issues) → records "progress" → skips the LLM supervisor
3. The backlog count stabilizes (remaining work is all status:blocked)
4. The stall timer starts counting from the last snapshot update
5. ~48 minutes later, the LLM supervisor finally runs, detects "status:blocked but blockers resolved", and transitions labels
6. The next deterministic fill floor cycle (2 min later) dispatches workers
7. Total delay: ~50 minutes per dependency layer

For a 15-task chain with 5 dependency layers, this means ~4 hours of idle time waiting for label transitions that are fundamentally a set membership check.

Why this is a deterministic operation

is_blocked_by_unresolved() (pulse-wrapper.sh:8630) already does the exact check: parse blocked-by:tNNN / blocked-by:#NNN from the issue body, check if the referenced issues are still open. This is a pure function — no judgment, no edge cases, no ambiguity. It belongs in the deterministic pass, not behind the LLM gate.

Proposed Solution: Cached Dependency Graph

A two-phase approach that separates the expensive graph construction (infrequent) from the cheap resolution check (every cycle).

Phase 1: Graph construction (infrequent, O(B) API calls)

Parse all status:blocked issue bodies for blocked-by references. Build a forward + reverse dependency map and cache it to disk:

{
  "built_at": 1775658447,
  "repo_slug": "owner/repo",
  "forward": {
    "105": [104],
    "106": [104, 105],
    "108": [101, 107],
    "109": [108]
  },
  "reverse": {
    "101": [108],
    "104": [105, 106],
    "105": [106],
    "107": [108],
    "108": [109]
  }
}

When to rebuild:

• On LLM supervisor runs (daily sweep or stall — the supervisor already reads issue state)
• On a standalone 1-hour cadence via file-based timestamp gate (decouples from LLM scheduling)
• Incrementally: only read bodies for issues added to status:blocked since last build

Data source options (in order of preference):

1. GitHub's native sub-issues/blocked-by GraphQL API — issue-sync-helper.sh already syncs addBlockedBy relationships (line 1094). Query these directly instead of parsing body text. Zero body reads needed.
2. Prefetch body parsing — if the prefetch is extended to include issue bodies (currently fetches number, title, url, assignees, labels, updatedAt only).
3. Dedicated body fetch — O(B) API calls where B = blocked issues. Acceptable at <500 issues.

Cache location: ${PULSE_DIR}/dependency-graph.json (per-repo, written atomically via temp+rename).

Phase 2: Graph resolution (every 2-min cycle, zero additional API calls)

The prefetch already fetches all open issues every cycle (for build_ranked_dispatch_candidates_json). That data contains the set of all open issue numbers. Checking "is blocker X closed?" = "is X absent from the open issues set?" — a pure set membership check.

resolve_blocked_by_graph():
  1. Read cached dependency graph from disk
  2. If cache is missing or stale (>2h), skip (LLM supervisor will rebuild)
  3. Build set of open issue numbers from prefetch data (already in memory)
  4. For each entry in forward map:
     - If ALL blockers are NOT in the open set → all resolved
     - gh issue edit --remove-label "status:blocked" --add-label "status:available"
     - Post comment: "Blockers resolved (#{X}, #{Y} closed). Unblocked for dispatch."
  5. Remove resolved entries from the cached graph (avoid re-checking)

Cost at scale:

Blocked issues	Graph edges	API calls per cycle	Computation
50	~80	0 (label swaps only when something changes)	~80 set lookups
500	~800	0	~800 set lookups
1,000	~1,500	0	~1,500 set lookups
5,000	~8,000	0	~8,000 set lookups

The only API calls are the label transition itself (one gh issue edit per newly-unblocked issue), and those only fire when something actually changes — typically 1-3 per cycle at most.

Simpler Variant: Event-Driven Post-Merge Forward-Unblock

Instead of scanning the full graph every cycle, trigger resolution only when the merge pass closes an issue:

After merge_ready_prs_all_repos() closes issue X:
  1. Look up X in the reverse map → get downstream issues [Y, Z]
  2. For each downstream issue:
     - Check if ALL its blockers (from forward map) are closed
     - If yes → swap labels
  3. Next fill floor cycle (2 min) dispatches worker for Y/Z

Cost per merge: O(D) where D = number of downstream issues of the closed blocker. Typically 1-3. Scales with merge volume, not total issue count.

Trade-off: More targeted (fewer checks) but only triggers on PR merges. Issues closed without a PR merge (manual close, duplicate, etc.) wouldn't trigger forward-unblock until the next graph scan. The full graph scan (Phase 2) could run at a lower frequency (every 10 min) as a catch-all.

Recommendation: Implement both — event-driven forward-unblock for the fast path, periodic graph scan as the safety net.

Integration point in main()

main()
├── _run_preflight_stages()           # existing — fetches open issues
├── merge_ready_prs_all_repos()       # existing — merges ready PRs
│   └── (event-driven forward-unblock after each merge)  # NEW
├── resolve_blocked_by_graph()        # NEW — periodic graph scan (~60 lines)
│   ├── read cached dependency graph
│   ├── build open-issue set from prefetch data
│   ├── for each blocked issue: check if all blockers ∉ open set
│   └── swap labels for newly-resolved issues
├── apply_deterministic_fill_floor()  # existing — dispatches available issues
└── (LLM supervisor, if triggered)
        └── rebuild_dependency_graph() as side effect  # NEW (~40 lines)

Cache Maintenance and Staleness

• Rebuild triggers: LLM supervisor run, standalone 1-hour cadence, new status:blocked issue detected in prefetch
• Incremental updates: Track last_build_epoch and only read bodies for issues with updatedAt > last_build_epoch
• Staleness bound: Worst case, a newly-blocked issue waits one rebuild cycle (1 hour) before entering the graph. The LLM supervisor remains the safety net for anything the cache misses.
• Invalidation: When resolve_blocked_by_graph() transitions labels, remove the entry from the forward map and write back. Prevents re-processing.

Risk Assessment

Low risk:

• Resolution is a pure deterministic check — no judgment, no model calls, no new edge cases
• Uses existing is_blocked_by_unresolved() parsing logic for graph construction
• Label swaps are the same operation the LLM supervisor already performs
• Cache staleness is bounded and the LLM supervisor remains the safety net
• Zero impact on the LLM supervisor path — it can still handle blocked-by if it runs

Potential concerns and mitigations:

• Stale cache serves wrong data: Bounded by rebuild cadence (1h). Worst case: an issue stays blocked one extra hour — same as current behavior.
• Race condition on label swap: The dispatch dedup guards (7-layer) already handle this. A label swap during an active dispatch cycle is safe.
• Graph construction cost at scale: With GitHub native sub-issues API as the data source, graph construction is a single GraphQL query — not O(B) REST calls. Fall back to body parsing only if native API is unavailable.

Files to Modify

• EDIT: .agents/scripts/pulse-wrapper.sh — add resolve_blocked_by_graph(), integrate into main() between merge pass and fill floor
• EDIT: .agents/scripts/pulse-wrapper.sh — add rebuild_dependency_graph(), call from LLM supervisor post-run and standalone cadence
• EDIT: .agents/scripts/pulse-wrapper.sh — extend merge_ready_prs_all_repos() with event-driven forward-unblock hook
• NEW: ${PULSE_DIR}/dependency-graph.json — cached graph (runtime artifact, not committed)

Verification

# After implementation, on a repo with blocked-by chains:
# 1. Create two issues: A (no blockers) and B (blocked-by A)
# 2. Let the pulse dispatch a worker for A
# 3. Worker completes A, PR merged, issue closed
# 4. Within 2-4 minutes (not 1 hour): B should have status:available
# 5. Next fill floor cycle dispatches worker for B

# Verify graph cache:
cat "${PULSE_DIR}/dependency-graph.json" | jq .

# Verify resolution log:
grep "resolve_blocked_by_graph\|forward-unblock" "$LOGFILE"

Environment

• aidevops: 3.6.174
• AI Assistant: Claude Code (claude-opus-4-6)
• OS: Ubuntu 24.04.4 LTS
• Shell: bash 5.2.21
• gh CLI: 2.89.0

Related

• pulse-wrapper.sh:8630 — is_blocked_by_unresolved() — existing blocked-by parser (reuse for graph construction)
• pulse-wrapper.sh:9429 — _should_run_llm_supervisor() — the stall gate that causes the delay
• pulse-wrapper.sh:10512-10526 — deterministic merge pass + fill floor — the integration point
• issue-sync-helper.sh:1048-1102 — GitHub native sub-issues sync (addBlockedBy GraphQL) — potential data source
• pulse.md:275 — "status:blocked but blockers resolved → remove label, add status:available" — the LLM instruction being moved to deterministic
• GH#17779 — _is_task_committed_to_main() false positive fix — related blocked-by dispatch bug

---

Review Notes (Approved — tier:standard)

Reviewer: claude-opus-4-6 via /review-issue-pr

Validation

• Reproducible: Yes — code confirms _should_run_llm_supervisor() (line 9598) gates blocked-by resolution behind a 1h stall threshold. is_blocked_by_unresolved() (line 8799) is only called defensively at dispatch time to skip blocked issues — it never transitions labels.
• Not duplicate: Confirmed. No prior issues address deterministic blocked-by resolution. GH#17779 is related but distinct.
• Classification: Enhancement (current behavior is by design; the issue correctly identifies it should be deterministic).

Design Corrections

1. addBlockedBy GraphQL claim is inaccurate. The issue states issue-sync-helper.sh already syncs addBlockedBy relationships at line 1094. This doesn't exist in the current codebase (1462-line file, no blocked/dependency/addBlocked references). Drop data source option 1 from the plan. Body parsing (option 3) is the realistic data source.

2. The "progress suppresses LLM" paradox is the real insight. Line 9654 shows that when total_now < total_before, the snapshot is updated and the LLM is skipped. Workers completing tasks = fewer open issues = "progress" = LLM suppressed. But the remaining issues are all status:blocked and can't progress without the LLM. This is the core bug — call it out in commit messages.

Implementation Guidance for Worker

Approach: self-maintaining dependency graph (per @robstiles addendum). The incremental maintenance makes the graph cheap enough that a cache-less intermediate step adds no value. Implement the full graph lifecycle directly:

1. Cold start (no cache): Fetch ALL status:blocked issue bodies (one-time O(B) cost). Build forward + reverse maps. Write to ${PULSE_DIR}/dependency-graph.json.
2. Steady state (every 2-min cycle, ~0 API calls): Read cached graph. Diff prefetch labels against graph — fetch body only for newly status:blocked issues (typically 0-1). Remove entries no longer status:blocked. Run resolution check against open-issue set from PULSE_PREFETCH_CACHE_FILE (already on disk, zero API calls). Swap labels for resolved issues.
3. Supervisor backstop: Full graph rebuild on LLM supervisor runs. Catches body edits, manual label changes, drift. Overwrites cache — consistency check, not primary path.
4. Event-driven forward-unblock: After merge_ready_prs_all_repos() closes an issue, look up its reverse map entries and check if downstream issues are now fully unblocked.

Integration point: Between merge_ready_prs_all_repos() and apply_deterministic_fill_floor() in main() (~line 10688).

Data source: PULSE_PREFETCH_CACHE_FILE (~/.aidevops/logs/pulse-prefetch-cache.json) for the open-issue set. Issue bodies via gh issue view for blocked-by parsing (reuse is_blocked_by_unresolved() logic).

Known limitations (acceptable):

• Cross-repo blocked-by not supported (existing limitation, separate enhancement)
• Body edits between graph rebuilds not detected until next supervisor run (rare in practice)
• Stall detection unaffected — label swaps don't change open issue count

Tier: tier:standard — straightforward engineering using existing functions and data sources, no novel design needed.

[github-actions]

Thanks for the report, @robstiles.

This issue has been queued for maintainer review. A maintainer will review it and either:

• Approve it using: sudo aidevops approve issue 17871
• Ask for more information if the report needs clarification
• Close it if it's a duplicate or out of scope

Tip: For faster, higher-quality issue reporting, use /log-issue-aidevops in your AI assistant session. It gathers diagnostics automatically and checks for duplicates.

[robstiles]

Addendum: Self-Maintaining Graph Lifecycle

The original proposal frames graph construction as dependent on LLM supervisor runs or a standalone 1-hour cadence. This creates a chicken-and-egg problem: the graph exists to eliminate the 1-hour delay, but if the graph itself is only rebuilt on an hourly cadence, newly-blocked issues still wait up to 1 hour to enter the graph.

The better approach is a self-maintaining graph where the deterministic pass incrementally updates the cache every 2-min cycle:

Full lifecycle

Cycle 1 (cold start, no cache):
  → No graph exists on disk
  → Fetch ALL status:blocked issue bodies (one-time O(B) cost)
  → Build forward + reverse maps, write to disk
  → Run resolution check → unblock any already-resolved issues

Cycle 2-N (steady state, ~0 API calls):
  → Read cached graph from disk
  → Compare prefetch labels against cached graph:
      new_blocked = issues with status:blocked in prefetch BUT NOT in graph
      removed = issues in graph BUT NO LONGER status:blocked in prefetch
  → For new_blocked (typically 0-1 per cycle):
      Fetch the single issue body, parse blocked-by, add to graph
  → For removed:
      Delete from graph (zero API calls)
  → Resolution check:
      Build open-issue set from prefetch cache (already fetched)
      For each graph entry: all blockers ∉ open set? → swap labels
  → Write updated graph back to disk

Supervisor runs (hourly stall or daily sweep):
  → Full graph rebuild from all status:blocked issue bodies
  → Catches drift: edited issue bodies, manual label changes,
    issues that bypassed issue-sync
  → Overwrites cached graph — consistency check, NOT primary path

Key property

The graph is eventually consistent with a 2-minute bound for the common case (new blocked issues added, blockers completed). The supervisor provides strong consistency on its own cadence as a backstop. The system never depends on the supervisor for timely unblocking.

Concrete data source: prefetch cache

The "open issue set" used for resolution is already available in PULSE_PREFETCH_CACHE_FILE (~/.aidevops/logs/pulse-prefetch-cache.json). This is a JSON object keyed by repo slug, containing full issue lists with delta updates (GH#15286). The resolution step should read directly from this file — zero additional API calls.

cache structure (already exists):
{
  "owner/repo": {
    "last_prefetch": "2026-04-08T14:05:00Z",
    "issues": [{"number": 101, "labels": [...], ...}, ...],
    "prs": [...]
  }
}

The issues array contains all open issues with labels. Building the open-issue number set is a single jq pass over data already on disk.

Known limitations (acceptable)

1. Cross-repo blocked-by: is_blocked_by_unresolved() only resolves blockers within the same repo (line 8821: gh issue list --repo "$repo_slug"). The graph inherits this. Cross-repo blocked-by references would need the blocker's repo slug in the reference format (e.g., blocked-by:owner/repo#NNN), which isn't currently supported. This is a separate enhancement if needed.

2. Body edit detection: If someone edits an issue body to change blocked-by references between graph rebuilds, the deterministic pass won't detect it (it only watches for label additions/removals via prefetch). The next supervisor rebuild catches this. In practice, blocked-by references are set once at issue creation and rarely edited — acceptable staleness window.

3. Stall detection interaction: Unblocking issues (label swap) doesn't change the open issue count — only worker completions (issue closures) do. So the stall detection logic continues to work correctly without modification. The sequence is: deterministic pass unblocks → fill floor dispatches → worker completes → issue count decreases → stall detection sees "progress."

[marcusquinn]

Maintainer Approval (cryptographically signed)

APPROVE:issue:marcusquinn/aidevops:17871:2026-04-08T15:19:11Z

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[robstiles]

Addendum: Multi-Repo Scaling Analysis (50-1,000 repos)

The broader constraint: existing pulse API budget at scale

Before evaluating the dependency graph's scaling, the existing deterministic pass already has a budget problem at 50+ repos. Both _should_run_llm_supervisor() and list_dispatchable_issue_candidates_json() make fresh API calls every 2-min cycle — they don't read from the prefetch cache.

Operation	Per-cycle (50 repos)	Per-hour (30 cycles)	% of 5,000/hr
Stall detection (_should_run_llm_supervisor)	100 calls (2/repo)	3,000	60%
Dispatch candidates (list_dispatchable_issue_candidates_json)	50 calls (1/repo)	1,500	30%
Merge pass (PR list + checks)	~50-100	~1,500-3,000	30-60%
Existing total	200-250	6,000-7,500	120-150%

At 50 repos, the existing system exceeds the GitHub API rate limit (5,000/hr authenticated) before the dependency graph adds a single call. This is a pre-existing concern that the graph proposal should not worsen and should explicitly flag.

Dependency graph cost profile

The graph is inherently scale-friendly because of the construction/resolution separation:

Graph operation	Per-cycle (50 repos)	Per-hour	Scales with
Resolution	0 API calls	0	Edge count (compute only)
Incremental maintenance	0-50 body reads	0-1,500	New blocked issues
Label swaps	0-5	0-150	Unblocking events
Cold start (one-time)	2,500 body reads	N/A	Total blocked issues

At 1,000 repos with 10,000 blocked issues and 15,000 edges: resolution is still zero API calls — 15,000 set lookups completing in under a second. The bottleneck is never the graph resolution; it's the graph construction and the system's shared API budget.

Required: API budget management (system-wide, not graph-specific)

The dependency graph should integrate with a budget-aware system. Five design requirements:

1. Budget partitioning. Allocate the 5,000/hr across operations. Suggested split:

Operation	Budget	Notes
Prefetch (stall + candidates)	2,000/hr	Should migrate to cached reads (see #5)
Merge pass	1,000/hr	Already delta-cached for PRs
Dispatch (dedup, launch)	1,000/hr	Per-issue cost, scales with worker count
Graph maintenance	500/hr	Body reads for new blocked issues
Reserve	500/hr	Burst headroom, LLM supervisor

The graph draws from a dedicated allocation. When budget is low, graph construction defers — resolution (zero cost) always runs.

2. Cold start staggering. 50 repos × 50 blocked issues = 2,500 body reads. Build 5 repos' graphs per cycle, complete in 10 cycles (~20 min). Repos without a graph yet fall back to current behavior (LLM supervisor handles unblocking). No degradation during ramp-up.

3. Per-cycle caps. Hard limit on body reads per cycle for graph maintenance (e.g., max 10). Prevents burst consumption when many repos receive new blocked issues simultaneously. At steady state with 50 repos and sporadic new blocked issues, this cap is rarely hit.

4. Rate limit awareness. Check X-RateLimit-Remaining (from gh api rate_limit) before expensive operations. When remaining budget is below a threshold (e.g., <500), skip graph construction for that cycle. Resolution (zero API calls) runs unconditionally.

# Lightweight budget check (~1 API call, cacheable for 60s)
remaining=$(gh api rate_limit --jq '.resources.core.remaining')
if [[ "$remaining" -lt 500 ]]; then
    echo "[pulse-wrapper] API budget low ($remaining remaining) — deferring graph construction" >>"$LOGFILE"
    # Resolution still runs (zero cost)
fi

5. Stale data reuse for stall detection (independent optimization). _should_run_llm_supervisor() currently makes 2 fresh API calls per repo per cycle (100 calls/cycle at 50 repos = 3,000/hr — 60% of the entire budget). If it used issue/PR counts from the prefetch cache instead of fresh calls, that frees 3,000 calls/hr. This isn't a graph concern, but it's a prerequisite for any 50-repo deployment. The graph proposal should call it out as a dependency or co-requisite.

Scaling verification matrix

Repos	Blocked issues	Edges	Cold start	Steady state/hr	Resolution/cycle	Verdict
1	35	50	35 reads	~30	50 lookups	Trivial
10	200	300	200 reads	~150	300 lookups	Fine
50	1,000	1,500	Stagger over 20 min	~500	1,500 lookups	Fine with budget mgmt
100	3,000	5,000	Stagger over 40 min	~1,000	5,000 lookups	Needs stall detection caching
1,000	10,000	15,000	Stagger over 2h	~2,000	15,000 lookups	Needs full budget partitioning

The graph resolution column is always zero API calls — the numbers are in-memory set lookups. Even at 15,000 edges, this completes in <1 second in bash.

Graph storage at scale

Per-repo graph file: ~50 blocked issues × ~100 bytes/entry = ~5KB. At 1,000 repos: ~5MB total disk. Trivial. JSON parse time with jq: <100ms per file. At 50 repos processed sequentially: <5 seconds total.

Failure modes at scale

Failure	Impact	Mitigation
API rate limit exhausted	Graph construction deferred	Resolution still runs (zero cost). Falls back to LLM supervisor for new blocked issues.
Cache file corrupted	Graph unavailable for one repo	Cold-rebuild on next cycle. Other repos unaffected (per-repo files).
Prefetch cache stale	Resolution uses stale open-issue set	Worst case: one cycle delay (2 min). Prefetch refreshes next cycle.
Graph construction takes >2 min	Overlaps with next cycle	Per-cycle cap prevents runaway. Construction is interruptible (progress saved per-repo).
Race condition (two cycles write same graph)	One cycle's writes lost	Caught next cycle — resolution re-checks and re-transitions if needed.

Summary

The dependency graph proposal is inherently scale-friendly: resolution is O(E) compute with zero API calls, and construction is O(B) API calls amortized over infrequent rebuilds. The real scaling bottleneck at 50+ repos is the existing pulse system's API consumption (stall detection + dispatch candidates = 4,500/hr fresh calls). The graph should be designed as a good citizen of a shared API budget, with per-cycle caps, rate limit awareness, and staggered cold start. The co-requisite optimization (caching stall detection counts) benefits the entire pulse system, not just the graph.

[marcusquinn]

DISPATCH_CLAIM nonce=083a8e3fee3575f4e58ccb76390ab60b runner=marcusquinn ts=2026-04-08T16:31:44Z max_age_s=1800