agento magic prompt

gistfile1.txt

Single smartest addition to the plan right now:

  Add a closed-loop “Value Router” (autonomous work allocator) that scores backlog items by expected merge value per agent-hour, then dynamically spawns/pauses/
  resumes sessions based on live outcomes.

  Why this is the highest-leverage gap:

  - You already have backlog auto-claim and decomposition, but it is mostly label/FIFO + fixed capacity (MAX_CONCURRENT_AGENTS = 5) in services.ts.
  - You already collect rich telemetry (success/failure, reasons, durations) in observability, but it is not yet feeding scheduling decisions
    (observability.md).
  - You already have reaction/lifecycle machinery; this would become the optimizer layer above it.

  What makes it radically accretive:

  - It turns AO from “automation runner” into a self-optimizing execution system.
  - Every run improves future prioritization (which issue types, repos, agents, and decomposition patterns actually ship fastest with least rework).
  - It compounds throughput and merge quality without increasing operator overhead.

  If you want, I can draft the minimal V1 spec and implement the first scorer + scheduler loop behind a config flag.

Use a normalized expected-value score per candidate issue:

  MergeValue = (P_merge_24h * BusinessImpact * StrategicFit * Confidence) / ExpectedAgentHours

  Practical breakdown:

  - P_merge_24h (0..1): model from your own history by repo/label/agent/type.
      - Signals: similar issue merged rate, CI pass-on-first-try rate, review churn, reopen rate.
  - BusinessImpact (1..5): configurable weight from labels/metadata.
      - Example: customer-facing=5, reliability=4, tech-debt=2.
  - StrategicFit (0.8..1.3): multiplier for current priorities.
      - Example: if this sprint targets onboarding, onboarding-labeled issues get +30%.
  - Confidence (0.6..1.2): evidence quality factor.
      - Clear acceptance criteria/tests/decomposition quality increase it.
  - ExpectedAgentHours: predicted completion time from past runs.
      - Include expected retries from CI/review loops.

  Then schedule by descending MergeValue subject to constraints:

  - max parallel sessions,
  - fairness per project,
  - dependency/blocked checks.

  Start simple:

  1. Hand-tuned weights + rule-based estimates.
  2. Log predicted vs actual.
  3. Replace each component with learned models once you have enough data.