Stage 5: Action Plan

Triage each insight and produce a concrete, prioritized action plan.

Inputs

• eval/stage1_insights_summary.md

  — insights from Kayba

• eval/stage2_domain_context.md

  — domain context

• eval/baseline_metrics.md

  — the evaluation rubric

• eval/baseline_metrics.json

  — baseline values

• eval/compute_baselines.py

  — measurement code

Read all files before starting.

Process

1. Triage each insight

For each insight/skill, answer three questions in order: Is it valid? Is it already handled? Is it a code fix or prompt fix?

1a. Validity check

• Does it describe a real, recurring problem visible in traces — or noise from a one-off edge case?
• Is it actionable — can the agent actually change this behavior given its tools and context?
• If not valid → verdict: discard with a one-sentence reason.

1b. "Already handled" verification

Do not rely on memory or assumption. Run these checks and cite what you find:

1. Grep the codebase for 2-3 key terms from the insight (tool names, error strings, behavioral keywords). Example: for an insight about cancellation eligibility, grep for

   cancel

   ,

   eligibility

   ,

   criteria

   .
2. Read the existing system prompt text — check

   AGENT_INSTRUCTION

   in the agent file and the domain policy file. Quote any existing language that addresses this behavior.
3. Verdict:
   • If existing text partially covers it → keep as a strengthening fix, note what's missing.
   • If no existing coverage → keep.
   • If existing prompt text already covers the behavior thoroughly AND the baseline metric is >= 95% → discard (cite the existing text and metric). A high baseline alone is NOT sufficient to discard — if the metric is below 95%, there are still failures to fix. An 87% baseline means 1 in 8 attempts still fails; that is worth fixing.

1c. Code-vs-prompt decision tree

Walk through this tree for every non-discarded insight:

Q1: Can the agent fix this by following different instructions?
    (Does it have the right tools, correct data in tool responses,
     and sufficient context to behave correctly?)
  │
  ├─ YES → PROMPT FIX
  │        The agent has everything it needs but acts wrong.
  │        A system prompt addition would fix it.
  │
  └─ NO → Q2: What is the agent missing?
           │
           ├─ Tool doesn't exist, schema is wrong, API returns
           │  incomplete data, infrastructure drops information,
           │  timeout/error not surfaced to agent
           │  → CODE FIX
           │    Name the file, function, and specific change.
           │
           └─ The agent has partial information but the prompt
              can't fully compensate (e.g., needs a new tool
              but a heuristic prompt workaround exists)
              → PROMPT FIX (primary) + CODE FIX (optional)
                Note both. Mark the code fix as "optional" with
                a one-sentence justification for why it's lower priority.

Ambiguity default: When genuinely uncertain, default to prompt fix and add a note:

"Classification uncertain — defaulting to prompt fix. Revisit if prompt change doesn't move metrics."

This is safer because prompt fixes are cheaper to test and revert, and Stage 7 handles prompt fixes and code fixes through different paths.

Use the reflector's reasoning from Stage 1 insights — it often explicitly identifies root causes that clarify the code-vs-prompt distinction.

2. Consolidate related insights

Before writing recommendations, merge insights that are redundant. Two insights should merge when ALL three conditions hold:

1. Same target behavior — they describe the agent doing (or failing to do) the same thing.
2. Overlapping fix text — the prompt instructions you'd write for each would share >50% of their content.
3. Addressing one substantially addresses the other — fixing insight A would fix >80% of the cases described by insight B.

When NOT to merge — two insights about the same tool or domain area but different failure modes should remain separate. Example: "agent doesn't check cancellation eligibility" and "agent doesn't execute cancellation after user confirms" both involve

cancel_reservation

but are completely different behavioral failures with different prompt fixes. Keep them separate.

For each merge, document:

• Which insight IDs are combined
• Which insight's framing is primary (use the one with stronger trace evidence)
• What, if anything, is lost from the secondary insight (add it as a sub-point)

3. Write specific recommendations

For each insight (after merging):

• Discards: one sentence on why it's not valid or actionable.
• Code fixes: what code/schema/infrastructure to change. Name the file, the function, the specific change. If Stage 7 needs to find the right code location, give it enough to grep for.
• Prompt fixes: the exact instruction text to add to the system prompt, where it should go (e.g., appended to

  AGENT_INSTRUCTION

  , added to domain policy, or as a standalone skill block), and why this wording over alternatives.

4. Assess risk per fix

For each non-discarded fix, assess whether the change could break currently-working behaviors:

Risk	Definition	Example
None	Change is additive; no existing behavior could be affected	Adding a new metric to compute_baselines.py
Low	Change targets a behavior that is currently failing; working cases are unrelated	Adding a cancellation checklist when current cancellation compliance is 0%
Medium	Change modifies a behavior where some cases already work correctly	Strengthening confirmation protocol when 28.6% already succeed — could the new wording break the working 28.6%?
High	Change rewrites or constrains a behavior that mostly works	Restricting tool-call patterns when 41.4% already comply — overly rigid wording could cause the agent to under-call tools

For Medium and High risk fixes, add a one-sentence mitigation: what to watch for, or how to word the prompt to preserve working cases.

5. Handle qualitative-only insights — STILL PRODUCE FIXES

Some insights from Stage 3 may be flagged as "unmeasurable." These still get fixes. An insight that the agent fabricates data or violates policy is a real problem whether or not we can measure it programmatically. Treat them the same as any other insight:

• Run the same triage (validity → already-handled → code-vs-prompt) as every other insight.
• Include them in the priority-ranked implementation list alongside all other fixes. They are NOT second-class.
• Use the trace evidence from the insight (not the metric) to assess impact and priority. If the insight has strong trace evidence showing clear failures, rank it accordingly.
• For prioritization: since there is no metric denominator, use confidence = 0.5 and estimate impact from the severity described in the insight evidence.
• In the fix entry, note that this fix has no programmatic metric for automated before/after comparison, so improvement should be verified via manual trace review or LLM-as-judge after generating new traces.

Only relegate an insight to a non-actionable "Monitor Items" section if the triage concludes it should be discarded (not valid or not actionable). Being unmeasurable is NOT a reason to skip fixing it.

6. Link to metrics

For each non-discarded fix, identify which metric(s) from the rubric would move if this fix is implemented. Use the metric IDs from

eval/baseline_metrics.md

(e.g., M1, M2).

7. Prioritize

Rank non-discarded fixes using this formula:

Priority Score = Impact × Confidence × Tier Bonus ÷ Risk Factor

Where:

• Impact = estimated metric delta. Use the gap between baseline and 100% as the ceiling. A fix expected to close 50% of that gap on M1 (baseline 41.4%) has impact = 0.5 × (1.0 - 0.414) = 0.293.
• Confidence = sample size reliability. Use the denominator from

  baseline_metrics.json

  :
  • denominator >= 20: confidence = 1.0
  • denominator 10-19: confidence = 0.8
  • denominator 5-9: confidence = 0.6
  • denominator < 5: confidence = 0.3
• Tier Bonus = leading metrics get a 1.5x multiplier (they validate adoption), lagging and quality get 1.0x. Rationale: leading metrics move first and tell you if your fix is even being adopted — you want those signals early.
• Risk Factor = None: 1.0, Low: 1.0, Medium: 1.5, High: 2.0

You do not need to compute exact scores to three decimal places. The formula is a tiebreaker and sanity check. The point is:

• High-impact, high-confidence, leading-metric fixes with low risk go first.
• Low-confidence fixes (small denominators) get deprioritized even if the metric is at 0%.
• High-risk fixes get deprioritized unless impact is overwhelming.

After scoring, apply one manual adjustment pass: if a fix is a prerequisite for another fix (e.g., "confirmation protocol" must exist before "post-confirmation execution" can be measured), promote the prerequisite even if its standalone score is lower.

Output format

Write to

eval/action_plan.md

:

# Action Plan

## Summary
- Total insights: N
- Discarded: X (with reasons)
- Code fixes: Y
- Prompt fixes: Z
- Fixes without programmatic metric (verify manually): Q

## Implementation Priority
| Rank | Fix | Type | Metrics | Risk | Score rationale |
|------|-----|------|---------|------|-----------------|
| 1 | [name] | prompt | M1, M2 | Low | [one-line: why this ranks here] |
| 2 | ... | ... | ... | ... | ... |

---

## Skill: [insight ID(s)] — [title]
**Summary:** [one-line description of what the skill addresses]
**Verdict:** `prompt fix` | `code fix` | `discard`
**Classification path:** [which branch of the decision tree — e.g., "Agent has tools and data but acts wrong → prompt fix"]
**Rationale:** [why this verdict — reference specific trace evidence from insights]
**Risk:** None | Low | Medium | High — [one-sentence justification]
**Risk mitigation:** [for Medium/High only — what to watch for or how to preserve working cases]
**Recommendation:** [specific change to make]
**Files to modify:** [list of files, for code fixes]
**Metric link:** [which metrics would move, with baseline values]
**Already-handled check:** [what you grepped, what existing prompt text you found, verdict]

---
[repeat for each insight]

## Consolidated Prompt Skills
[After all per-insight entries, list the final merged prompt skill texts in priority order, ready for Stage 7 to implement]

## Monitor Items (Non-Actionable Only)
[Only insights that were triaged as genuinely non-actionable — e.g., the agent cannot change this behavior, or the insight is noise. Unmeasurable insights that are still real problems should appear in the priority list above, NOT here.]

Group related insights under cluster headings when they address the same underlying behavior. For merged insights, list all constituent insight IDs in the heading.

Outputs

• eval/action_plan.md