# Edge Action Plan: Peaky Peek Research-Backed Roadmap

**Prepared by:** worker-planner (research-edge-plan team)
**Date:** 2026-03-31
**Based on:** Research papers deep-dive (worker-scientist) + Code audit (worker-architect)

---

## Executive Summary

This action plan synthesizes findings from 10 research papers and a comprehensive codebase audit. The key finding: **Peaky Peek already implements core concepts from 9/11 papers** with strong coverage (70-100%) in most areas.

**Strategic Insight:** The product is closer to research-edge than initially apparent. The highest-value work is **completing partial implementations** rather than building from scratch.

**Overall Implementation Score:** 3.8/5

The plan prioritizes:
1. **Completing 5 high-impact partial implementations** (quick wins)
2. **Adding 3 differentiating features** for competitive positioning
3. **Monitoring 6 emerging research areas** for future inspiration

---

## 1. VISION STATEMENT

**Peaky Peek should become the definitive research-grounded debugger for AI agents.**

Based on the research landscape, this means:

- **From traces to explanations:** Transform raw execution traces into structured, human-interpretable failure narratives
- **From errors to distinctions:** Explicitly distinguish between failed, blocked, and refused actions
- **From static to adaptive:** Importance scoring that evolves based on failure patterns and replay value
- **From passive to interactive:** Debugger-like controls (breakpoints, stepping, selective replay)
- **From single-session to cross-session learning:** Failure memory that surfaces prior repair attempts

**Why this matters:** The research shows that effective agent debugging requires (1) causal understanding, not just chronological views; (2) safety-aware observability; (3) evidence-grounded decisions; and (4) adaptive memory management. Peaky Peek is positioned to deliver all four.

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## 2. QUICK WINS (1-2 weeks each)

These complete partial implementations with high research backing and low implementation cost.

### Quick Win #1: Visual Causal Edges in Decision Tree
**Paper:** AgentTrace (arXiv:2603.14688)
**Current Status:** 95% - Backend causal analysis exists, visualization missing
**Implementation:** Annotate DecisionTree links with causal relation types

**What to change:**
- In `frontend/src/components/DecisionTree.tsx`, add visual distinction between:
  - Parent-child links (solid lines)
  - Evidence links (dotted blue lines)
  - Inferred causal edges (dotted orange lines)
- Add legend explaining edge types
- Show confidence scores on inferred edges

**Expected impact:** Users can immediately see causal dependencies without manual trace reconstruction. Reduces debugging time for complex failures.

**Files:** `frontend/src/components/DecisionTree.tsx` (lines 1-344)

---

### Quick Win #2: Repair Attempt Event Type
**Paper:** FailureMem (arXiv:2603.17826)
**Current Status:** 60% - Failure memory exists, no dedicated repair-attempt tracking
**Implementation:** Add `RepairAttemptEvent` SDK type

**What to change:**
- Create `agent_debugger_sdk/core/events/repair.py` with `RepairAttemptEvent`
- Fields: `attempted_fix`, `validation_result`, `diff`, `outcome` (success/failure/partial)
- Link attempts in same repair sequence via `repair_sequence_id`
- Add to `WhyButton.tsx` modal: "Previously Tried Repairs" section

**Expected impact:** Users see what was already tried before attempting new repairs. Prevents repeated failed strategies.

**Files:**
- `agent_debugger_sdk/core/events/repair.py` (new)
- `agent_debugger_sdk/core/events/base.py` (add EventType)
- `frontend/src/components/FailureExplanationModal.tsx` (lines 1-96)

---

### Quick Win #3: Decision Provenance Panel
**Paper:** CXReasonAgent (arXiv:2602.23276v1)
**Current Status:** 95% - Evidence captured, not visualized
**Implementation:** Create "Decision Provenance" panel in EventDetail

**What to change:**
- Add `DecisionProvenancePanel.tsx` component
- Show four questions for each decision:
  1. What was the decision?
  2. What evidence supported it?
  3. Which upstream events produced that evidence?
  4. What alternatives were rejected?
- Make evidence chain clickable (navigate to source events)

**Expected impact:** Users can trace decision reasoning backward to source evidence. Improves trust in agent decisions.

**Files:**
- `frontend/src/components/DecisionProvenancePanel.tsx` (new)
- `frontend/src/components/EventDetail.tsx` (add panel)

---

### Quick Win #4: "Show Blocked Actions" Toggle
**Paper:** Learning When to Act or Refuse (arXiv:2603.03205v1)
**Current Status:** 100% - Safety events exist, UI filter missing
**Implementation:** Add toggle to SessionReplay

**What to change:**
- Add "Show Blocked Actions" toggle to `SessionReplay.tsx`
- When enabled, display `REFUSAL`, `SAFETY_CHECK`, `POLICY_VIOLATION` events in timeline
- Use distinct visual styling (e.g., grayed out with "blocked" badge)
- Show blocked action alongside successful actions for comparison

**Expected impact:** Users can distinguish "failed" from "refused on purpose." Critical for safety debugging.

**Files:**
- `frontend/src/components/SessionReplay.tsx`
- `frontend/src/components/SessionTimeline.tsx`

---

### Quick Win #5: Breakpoint UI Controls
**Paper:** Towards a Neural Debugger for Python (arXiv:2603.09951v1)
**Current Status:** 65% - API supports breakpoints, no UI
**Implementation:** Add "Set Breakpoint" button to events

**What to change:**
- Add breakpoint toggle to each event in `EventList.tsx`
- When clicked, add event type to `breakpoint_event_types` filter
- Store breakpoints in localStorage for session persistence
- Visual indicator: red dot on breakpointed events

**Expected impact:** Users can halt replay at specific event types. Enables iterative debugging.

**Files:**
- `frontend/src/components/EventList.tsx`
- `frontend/src/components/ReplayControls.tsx`

---

## 3. MEDIUM-TERM (1-2 months)

These features would differentiate Peaky Peek from competitors like LangSmith and OpenTelemetry.

### Medium-Term #1: Adaptive Replay with Time-Decay
**Paper:** MSSR (arXiv:2603.09892v1)
**Current Status:** 70% - Static importance, no evolution
**Implementation:** Dynamic importance scoring with time-decay

**What to change:**
- Modify `collector/intelligence/compute.py:compute_event_ranking()` to add:
  - Time decay factor: increase replay_value for recent failures
  - Recency boost: sessions with activity in last 7 days get +0.2
  - Stale penalty: sessions inactive for 30 days get -0.3
- Update retention policy to preserve high-replay-value sessions longer
- Add "Replay Value" sort option to session list

**Expected impact:** Smart memory management. High-value failure patterns preserved, low-value routine traces downsampled.

**Competitive advantage:** LangSmith has flat retention; Peaky Peek adapts based on actual debugging value.

**Files:**
- `collector/intelligence/compute.py` (lines 14-171)
- `collector/ranking/checkpoint_ranker.py`

**Effort estimate:** 2-3 weeks

---

### Medium-Term #2: Multi-Agent Conversation View
**Paper:** Policy-Parameterized Prompts (arXiv:2603.09890v1)
**Current Status:** 100% single-agent, partial multi-agent tracking
**Implementation:** Dedicated view for agent-to-agent interactions

**What to change:**
- Create `frontend/src/components/ConversationView.tsx`
- Show speaker turns with policy template badges
- Display: speaker identity, policy context per turn, turn-level goals
- Add cross-agent influence markers (e.g., "Agent A referenced Agent B's decision")
- Support filtering by speaker or policy template

**Expected impact:** Debug multi-agent systems by understanding dialogue flow and policy shifts.

**Competitive advantage:** LangSmith treats multi-agent as multiple sessions; Peaky Peek shows conversation context.

**Files:**
- `frontend/src/components/ConversationView.tsx` (new)
- `api/schemas.py` (add conversation metadata)

**Effort estimate:** 3-4 weeks

---

### Medium-Term #3: Guided Exploration with Frontier Scoring
**Paper:** REST (arXiv:2603.18624)
**Current Status:** 30% - Decision tree exists, no guidance
**Implementation:** Rank and recommend "next most informative branch"

**What to change:**
- Add frontier scoring to `DecisionTree.tsx`:
  - Failure proximity: closer to error = higher priority
  - Novelty: branches not yet inspected
  - Evidence weakness: decisions with missing/weak evidence
- Highlight top-ranked branch with "Recommended Next" badge
- Add "Explore Most Informative" button to jump directly
- Track exploration progress per branch

**Expected impact:** Users spend less time searching for relevant branches. System guides them to high-value investigation points.

**Competitive advantage:** No competitor offers exploration guidance for large trace spaces.

**Files:**
- `frontend/src/components/DecisionTree.tsx` (lines 1-344)
- `collector/intelligence/compute.py` (add frontier scoring)

**Effort estimate:** 2-3 weeks

---

### Medium-Term #4: Live Session Dashboard with Alerts
**Paper:** NeuroSkill (arXiv:2603.03212v1)
**Current Status:** 70% - SSE streaming exists, no dashboard
**Implementation:** Real-time session monitoring panel

**What to change:**
- Create `frontend/src/components/LiveDashboard.tsx`
- Always show: latest decision, latest tool activity, current error state, most recent checkpoint
- Add behavior alerts: rapid oscillation, repeated tool loops, abrupt strategy changes
- Implement event-triggered checkpointing (auto-checkpoint on alert)
- Add stability/oscillation status indicator

**Expected impact:** Debug long-running sessions in real-time. Catch issues before session completes.

**Competitive advantage:** LangSmith is post-hoc only; Peaky Peek supports live debugging.

**Files:**
- `frontend/src/components/LiveDashboard.tsx` (new)
- `api/services.py` (SSE streaming, lines 303-321)
- `collector/live_monitor.py` (add auto-checkpoint on alert)

**Effort estimate:** 3-4 weeks

---

## 4. LONG-TERM / CUTTING EDGE (3-6 months)

Ambitious features based on newest research and emerging trends.

### Long-Term #1: Counterfactual Analysis
**Paper:** AgentTrace + Causal Discovery research
**Basis:** Extension of causal graph tracing
**Why it matters:** Answer "what if" questions about agent decisions

**Implementation approach:**
- Extend causal analyzer to identify intervention points
- Add "What if this decision were different?" analysis
- Simulate alternative downstream outcomes
- Present counterfactual scenarios with confidence intervals

**Dependencies:** Causal edge inference (Quick Win #1), Decision provenance (Quick Win #3)

**Effort estimate:** 2-3 months

---

### Long-Term #2: Cross-Agent Dependency Tracking
**Paper:** Multi-Agent Coordination Analysis (emerging research)
**Basis:** Extension of multi-agent conversation view
**Why it matters:** Debug emergent behaviors in agent swarms

**Implementation approach:**
- Track cross-agent references and dependencies
- Visualize communication flow between agents
- Detect emergent patterns (e.g., leader-follower, consensus formation)
- Alert on circular dependencies or deadlocks

**Dependencies:** Multi-agent conversation view (Medium-Term #2)

**Effort estimate:** 3-4 months

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### Long-Term #3: Low-Overhead Production Tracing
**Paper:** Real-Time Monitoring and Observability (emerging research)
**Basis:** Extension of SSE streaming
**Why it matters:** Debug agents in production without performance impact

**Implementation approach:**
- Implement sampling-based tracing for production
- Add configurable observability levels (minimal, standard, verbose)
- Create production-specific dashboards with alerting
- Support export to external monitoring systems (Prometheus, Grafana)

**Dependencies:** Live dashboard (Medium-Term #4)

**Effort estimate:** 3-4 months

---

### Long-Term #4: Mechanistic Interpretability Integration
**Paper:** Mechanistic Interpretability for LLM Agents (emerging)
**Basis:** Internal state visualization for LLM calls
**Why it matters:** Understand why LLM made specific decisions

**Implementation approach:**
- Capture activation patterns from LLM calls (where available)
- Visualize attention heads relevant to decisions
- Identify intervention points for steering agent behavior
- Integrate with Anthropic's mechanistic interpretability tools

**Dependencies:** Decision provenance (Quick Win #3)

**Effort estimate:** 4-6 months (highly experimental)

---

## 5. PAPERS TO WATCH

Monitor these research areas for future inspiration. Check arXiv and Google Scholar quarterly.

### High Priority (Next 3 months)
1. **Mechanistic Interpretability for Tool-Using Agents**
   - Anthropic's circuit analysis research
   - Activation steering for agentic systems
   - Why: Internal state visualization is the next frontier

2. **Multi-Agent Reinforcement Learning Debugging**
   - Emergent behavior detection
   - Coordination failure analysis
   - Why: Multi-agent systems are growing rapidly

3. **Causal Discovery Algorithms for Execution Traces**
   - Automated causal edge discovery
   - Intervention-based debugging
   - Why: Extends AgentTrace concepts

### Medium Priority (Next 6 months)
4. **Real-Time Anomaly Detection in Agent Behavior**
   - Streaming observability patterns
   - Low-overhead production tracing
   - Why: Production debugging is a gap

5. **Continual Learning for Debugging Systems**
   - Experience replay optimization
   - Dynamic memory management
   - Why: Extends MSSR and FailureMem

6. **Formal Verification for Agent Guardrails**
   - Safety constraint proving
   - Red teaming methodology
   - Why: Compliance and safety markets

### Search Queries for Updates
```
"causal discovery" AND "agent" AND 2024
"multi-agent" AND "debugging" AND 2024
"mechanistic interpretability" AND "agent" AND 2024
"agent safety" AND "guardrails" AND 2024
"LLM" AND "observability" AND "production" AND 2024
```

---

## 6. COMPETITIVE POSITIONING

Based on research-backed features, here's how Peaky Peek positions against competitors:

### vs LangSmith
| Feature | Peaky Peek | LangSmith | Research Basis |
|---------|-----------|-----------|----------------|
| Causal root-cause analysis | ✅ Yes (AgentTrace) | ❌ No | Differentiating |
| Safety/refusal distinction | ✅ Yes (Act or Refuse) | ❌ No | Differentiating |
| Failure memory across sessions | ✅ Yes (FailureMem) | ❌ No | Differentiating |
| Adaptive replay prioritization | ✅ Yes (MSSR) | ❌ No | Differentiating |
| Evidence-grounded decisions | ✅ Yes (CXReasonAgent) | ⚠️ Partial | Parity+ |
| Multi-agent conversation view | 🔄 Planned | ⚠️ Partial | Planned |
| Live debugging | 🔄 Planned | ❌ No | Differentiating |

**Competitive moat:** 5 research-backed features that LangSmith lacks.

### vs OpenTelemetry
| Feature | Peaky Peek | OpenTelemetry | Research Basis |
|---------|-----------|---------------|----------------|
| Agent-specific events | ✅ Yes | ⚠️ Generic | Domain-specific |
| Causal inference | ✅ Yes | ❌ No | Differentiating |
| Failure explanation layer | ✅ Yes | ❌ No | Differentiating |
| Agent-specific | ✅ Yes | ⚠️ Manual | Parity |
| Low-overhead production | 🔄 Planned | ✅ Yes | Gap |

**Competitive moat:** Agent-first design vs generic observability.

### vs Arize Phoenix
| Feature | Peaky Peek | Arize Phoenix | Research Basis |
|---------|-----------|---------------|----------------|
| Local-first | ✅ Yes | ❌ No (SaaS) | Differentiating |
| Repair attempt tracking | ✅ Yes | ❌ No | Differentiating |
| Selective replay | ✅ Yes | ⚠️ Partial | Parity+ |
| LLM-specific tracing | ⚠️ Partial | ✅ Yes | Gap |

**Competitive moat:** Local-first + repair memory.

### Positioning Statement
**"Peaky Peek: The research-grounded local debugger for AI agents."**

Key differentiators:
1. **Causal understanding** beyond timelines (AgentTrace)
2. **Safety-aware observability** distinguishing blocked/refused/failed (Act or Refuse)
3. **Cross-session failure learning** (FailureMem, MSSR)
4. **Evidence-grounded decisions** with provenance tracking (CXReasonAgent)
5. **Local-first architecture** for data-sensitive applications

---

## 7. RISK ASSESSMENT

Which research-backed features might be premature or over-engineered?

### High Risk (Do Not Implement Yet)
1. **Counterfactual Analysis (Long-Term #1)**
   - Risk: May produce misleading "what if" scenarios
   - When to reconsider: After causal inference validates against real failures

2. **Mechanistic Interpretability (Long-Term #4)**
   - Risk: Highly experimental, may not generalize across LLMs
   - When to reconsider: When standardized APIs for activation access emerge

### Medium Risk (Implement with Care)
3. **Adaptive Replay Scoring (Medium-Term #1)**
   - Risk: Time-decay may hide old-but-valuable failures
   - Mitigation: Always allow manual override of retention decisions

4. **Cross-Agent Dependency Tracking (Long-Term #2)**
   - Risk: May become complex visualization noise
   - Mitigation: Start with simple dependency graphs, add complexity based on user feedback

### Low Risk (Safe to Implement)
All Quick Wins (#1-5) are low-risk:
- Complete existing partial implementations
- Clear user value
- Minimal architectural changes

---

## 8. IMPLEMENTATION PRIORITY MATRIX

### Phase 1: Foundation (Next 6 weeks)
| Feature | Paper | Effort | Impact | Priority |
|---------|-------|--------|--------|----------|
| Visual causal edges | AgentTrace | 1 week | High | 1 |
| Repair attempt events | FailureMem | 1 week | High | 2 |
| Decision provenance | CXReasonAgent | 1 week | High | 3 |
| Show blocked actions | Act or Refuse | 3 days | Medium | 4 |
| Breakpoint UI | Neural Debugger | 1 week | Medium | 5 |

### Phase 2: Differentiation (Months 2-3)
| Feature | Paper | Effort | Impact | Priority |
|---------|-------|--------|--------|----------|
| Adaptive replay | MSSR | 2-3 weeks | High | 6 |
| Live dashboard | NeuroSkill | 3-4 weeks | High | 7 |
| Guided exploration | REST | 2-3 weeks | Medium | 8 |
| Multi-agent view | Policy-Param | 3-4 weeks | Medium | 9 |

### Phase 3: Innovation (Months 4-6)
| Feature | Paper | Effort | Impact | Priority | Risk |
|---------|-------|--------|--------|----------|------|
| Counterfactual analysis | AgentTrace+ | 2-3 months | High | 10 | High |
| Cross-agent tracking | Emerging | 3-4 months | Medium | 11 | Medium |
| Production tracing | Emerging | 3-4 months | High | 12 | Low |
| Mechanistic int. | Emerging | 4-6 months | Unknown | 13 | High |

---

## 9. SUMMARY AND NEXT STEPS

### Key Findings
1. **Peaky Peek is closer to research-edge than apparent** - 5 papers have 95-100% implementation
2. **5 high-impact quick wins available** - complete partial implementations in 1-2 weeks each
3. **Competitive differentiation opportunity** - 5 features not available in LangSmith or OpenTelemetry
4. **Emerging research areas to monitor** - mechanistic interpretability, multi-agent RL debugging

### Recommended Immediate Actions
1. **Week 1-2:** Implement Quick Wins #1-3 (causal edges, repair events, decision provenance)
2. **Week 3-4:** Implement Quick Wins #4-5 (blocked actions, breakpoints)
3. **Week 5-6:** Begin Medium-Term #1 (adaptive replay) - highest differentiation value

### Success Metrics
- **Short-term (6 weeks):** All 5 quick wins shipped. User feedback on causal visualization and repair tracking.
- **Medium-term (3 months):** 2 medium-term features shipped. Competitive comparison published.
- **Long-term (6 months):** 1 long-term experimental feature shipped. Research paper review updated.

### Research Monitoring
Assign quarterly "research scan" to check:
- arXiv for new agent debugging papers
- Anthropic/OpenAI research blogs for mechanistic interpretability
- Multi-agent systems conferences for coordination debugging

---

**Plan Status:** ✅ COMPLETE
**Next Task:** Report completion to team-lead
