[observability] Agentic Observability Report — 2026-04-27 #28682
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Date range analyzed: 2026-04-27 (single-day snapshot, all 138 runs occurred today)
Repository: github/gh-aw
Executive Summary
138 runs across 68 workflows completed today with no escalation-eligible episodes and zero MCP failures or blocked-request episodes at the episode level. The portfolio is broadly healthy. The single operationally notable signal is a transient
blocked_requests_increaseclassification on one AI Moderator run (since resolved to stable on the next run). Cost is highly concentrated: three workflows — [aw] Failure Investigator (6h), Schema Consistency Checker, and Documentation Unbloat — account for $11.02 of the total $13.58 billed (all via Anthropic/Claude; Copilot-engine costs are $0). The primary portfolio observation is that the repository runs a very broad set of workflows (68), many in thegeneral_automationandissue_responsedomains, with exploratory execution style and high token volume that is not always justified by the domain. The graph lineage is entirely flat (0 edges), meaning no DAG orchestration is being detected; all 138 episodes are standalone.Key Metrics
riskypoor_control_node_count=1each)overkill_for_agenticpatternlatest_successfallbackHighest Risk Episodes
No episodes are escalation-eligible. The single risk signal is:
risky(blocked_requests_increase). The following run reverted tostable(cohort_match baseline). This is a transient fluctuation, not a regression pattern. No action required.Two episodes show
poor_control_node_count = 1:Both are isolated occurrences. Neither crosses the 14-day escalation threshold.
Episode Regressions
No repeated regressions detected. The 138-episode sample is a single day, limiting regression visibility. Key observations:
Visual Diagnostics
1. Episode Risk-Cost Frontier
Decision: Schema Consistency Checker and [aw] Failure Investigator dominate the token frontier with zero risk signal — high cost but justified for their research/validation domains.
Why it matters: The frontier reveals no workflows combining both high cost AND high risk, which is the healthiest possible shape. Cost optimization (not risk mitigation) is the primary lever available. Note: Copilot-engine effective tokens appear as $0 in billing but do consume quota.
2. Workflow Stability Matrix
Decision: AI Moderator is the only repeat offender on
risky_run_rate; the matrix is otherwise uniformly clean, indicating no chronic control problems across the portfolio.Why it matters: The repository does not have broad instability — it has one workflow with a transient signal and two with isolated poor-control events. The dominant instability driver is
risky_run_rateconcentrated in AI Moderator, which self-corrected.3. Repository Portfolio Map
Decision: High-token workflows (Schema Consistency Checker, Failure Investigator, Documentation Unbloat, Go Fan) belong in
optimize; the large cluster of low-token, high-frequency workflows belongs inkeep; smoke/test workflows belong insimplify.Why it matters: The repository has a healthy core (
keepquadrant) carrying most of the run volume at low cost, with a small set of expensive-but-valuable research agents inoptimize. Thereviewquadrant contains candidates for right-sizing or deterministic replacement.4. Workflow Overlap Matrix
Decision: Contribution Check and Schema Consistency Checker show moderate overlap with each other and with [aw] Failure Investigator via shared
general_automation/exploratorybehavior cluster — worth reviewing for potential consolidation or pre-step extraction.Why it matters: The overlap is behavior-cluster-based, not confirmed by workflow definitions. It is suggestive rather than conclusive. Consolidation would require confirming trigger and scope alignment.
Portfolio Opportunities
Optimize (high-token, high-value — consider right-sizing):
Simplify / deterministic candidates (lean + directed + narrow domain):
/cloclo,Scout,Q,Archie— 9-10 runs each, 0 tokens (Copilot engine, no billing data), low action minutes, directed style. These run frequently at low overhead and appear narrow-scope. If they are reading/aggregating, they may be partially reducible to deterministic pre-steps.Review (potentially overlapping or weakly justified):
general_automation, exploratory style, high token count. Overlap in name family and schedule family; worth confirming whether they cover distinct dimensions or could be merged.View full workflow inventory (all 68 workflows)
Recommended Actions
No escalation required. Zero episodes are escalation-eligible. The AI Moderator
blocked_requests_increasesignal self-corrected.Investigate Visual Regression Checker errors (4 errors today, 2 runs). Not an agentic control problem — likely an environment dependency. Run a targeted
auditif errors persist tomorrow.Review Schema Consistency Checker token footprint. At 8.1M tokens for a single run, this is the highest per-run token cost in the repository. A deterministic schema-diff pre-step could significantly reduce agent scope.
Evaluate Daily CLI Performance Agent and Daily CLI Tools Exploratory Tester overlap. Both are daily, exploratory, general_automation. Confirm they cover distinct dimensions before the next schedule cycle.
Smoke family right-sizing. 8 Smoke workflows running at low token/low error rates. If they are pure pass/fail infrastructure checks, consider replacing agentic execution with deterministic CI steps.
Add DAG lineage instrumentation. 0 edges detected means no orchestrator→worker relationships are being captured. If any workflows delegate to others (e.g., Failure Investigator spawning sub-agents), enabling lineage tracking will improve future observability.
References:
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