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Phase 1: Risk Assessment & Mitigation Strategies

Version: 1.0 Date: 2025-11-12 Phase: Phase 1 - Proof of Concept Review Frequency: Weekly (Fridays during sprint review)

Executive Summary

Phase 1 faces moderate overall risk with no show-stoppers identified. Primary risk areas:

  1. Technical: Performance targets (Reflex Layer throughput)
  2. Security: Container escapes (Executor Arm)
  3. Schedule: Optimistic time estimates
  4. Quality: LLM hallucinations affecting planning accuracy

Risk Distribution:

  • Critical Risks: 1 (Container security)
  • High Risks: 3 (Performance, LLM reliability, Timeline)
  • Medium Risks: 8
  • Low Risks: 12

Overall Risk Score: 3.2/10 (Moderate)

Risk Register

Critical Risks

RISK-001: Container Escape Vulnerability

Category: Security Probability: LOW (15%) Impact: CRITICAL (10/10) Risk Score: 1.5/10

Description: Executor Arm's Docker sandbox could be compromised, allowing malicious commands to escape containerization and access host system.

Potential Impact:

  • Data breach (access to host filesystem)
  • System compromise (privilege escalation)
  • Reputation damage (security incident disclosure)
  • Project delay (requires security audit and re-architecture)

Indicators:

  • Security penetration tests fail
  • Container escape POC successful
  • Seccomp profile bypassed
  • Privilege escalation detected

Mitigation Strategy:

  1. Prevention:
    • Use gVisor (optional hardening layer) for enhanced isolation
    • Implement strict seccomp profile (allow minimal syscalls)
    • Drop all capabilities: CAP_NET_RAW, CAP_SYS_ADMIN, CAP_DAC_OVERRIDE
    • Run containers as non-root user (uid 1000)
    • Read-only filesystem with only /tmp writable
    • Command allowlisting (reject dangerous commands like mount, chroot)
  2. Detection:
    • Penetration testing by security engineer (Sprint 1.4)
    • Automated security scans (trivy, grype)
    • Runtime monitoring for anomalous behavior
  3. Response:
    • If escape found: Disable Executor Arm immediately
    • Emergency security sprint (1 week) to implement fixes
    • Third-party security audit if needed

Contingency Plan:

  • If High Severity Escape: Delay Phase 1 completion, bring in external security consultant
  • If Medium Severity: Fix in Phase 2, document limitations
  • If Low Severity: Document as known issue, fix incrementally

Owner: Security Engineer Review Frequency: Daily during Sprint 1.4

High Risks

RISK-002: Reflex Layer Performance Below Target

Category: Technical Probability: MEDIUM (40%) Impact: HIGH (7/10) Risk Score: 2.8/10

Description: Reflex Layer fails to achieve >10,000 req/sec throughput or <10ms P95 latency targets.

Potential Impact:

  • Bottleneck in system (limits overall throughput)
  • Increased infrastructure costs (need more instances)
  • Poor user experience (slow responses)
  • Architecture re-think (maybe Python instead of Rust?)

Indicators:

  • Benchmarks show <5,000 req/sec sustained
  • P95 latency >20ms
  • CPU bottlenecks identified in profiling

Mitigation Strategy:

  1. Prevention:
    • Early benchmarking (Sprint 1.1 Day 3)
    • Profiling with cargo flamegraph
    • SIMD optimization for string scanning (if applicable)
    • Lazy regex compilation (lazy_static)
    • LRU cache before Redis (L1 cache)
  2. Detection:
    • k6 load tests (Sprint 1.1.7)
    • Continuous benchmarking in CI
  3. Response:
    • If <8,000 req/sec: Pair Rust engineer with performance expert
    • If <5,000 req/sec: Evaluate Python async alternative
    • If not fixed: Deploy multiple reflex instances with load balancer

Contingency Plan:

  • If Unfixable: Use Python/FastAPI prototype (slower but acceptable for MVP)
  • If Fixable with Time: Extend Sprint 1.1 by 1 week
  • Cost Impact: +$7,200 (40h × $180/h)

Owner: Rust Engineer Review Frequency: Daily during Sprint 1.1

RISK-003: LLM Hallucinations in Planning

Category: Technical Probability: MEDIUM (50%) Impact: MEDIUM (6/10) Risk Score: 3.0/10

Description: GPT-3.5-Turbo produces invalid plans, circular dependencies, or nonsensical steps.

Potential Impact:

  • Low planning success rate (<70% vs 90% target)
  • User frustration (failed tasks)
  • Increased LLM costs (retries)
  • Need to upgrade to GPT-4 (10x cost increase)

Indicators:

  • Test scenarios fail >30%
  • Invalid JSON responses >10%
  • Circular dependency errors
  • User reports of bad plans

Mitigation Strategy:

  1. Prevention:
    • Detailed system prompt (400+ lines) with examples
    • JSON schema validation (Pydantic strict mode)
    • Response format: json_object (OpenAI structured output)
    • Temperature: 0.3 (reduce randomness)
    • Topological sort validation (reject circular deps)
  2. Detection:
    • Automated testing on 30 diverse scenarios
    • Confidence scoring (flag low-confidence plans)
    • Manual review of first 50 production plans
  3. Response:
    • If <70% success: Improve system prompt, add few-shot examples
    • If <50% success: Upgrade to GPT-4 (accept cost increase)
    • Implement human-in-the-loop for critical tasks

Contingency Plan:

  • If GPT-3.5 Insufficient: Budget $150 extra for GPT-4 testing
  • If Persistent Issues: Implement fallback to rule-based planner (predefined templates)

Owner: Python Engineer (Senior) Review Frequency: Daily during Sprint 1.3

RISK-004: Schedule Slip (Optimistic Estimates)

Category: Schedule Probability: HIGH (60%) Impact: MEDIUM (5/10) Risk Score: 3.0/10

Description: 8.5 week estimate is optimistic; actual delivery takes 10-12 weeks.

Potential Impact:

  • Delayed Phase 2 start
  • Budget overrun (+$15k-30k labor)
  • Team morale impact (crunch time)
  • Stakeholder dissatisfaction

Indicators:

  • Sprint velocity <80% of planned
  • Sprint 1.1 takes 3 weeks instead of 2
  • Frequent scope creep requests
  • Unplanned blockers (infrastructure, LLM API issues)

Mitigation Strategy:

  1. Prevention:
    • 20% buffer built into estimates (500h includes 80h buffer)
    • Weekly velocity tracking (actual vs planned hours)
    • Ruthless scope prioritization (MVP only)
    • Daily standups to surface blockers early
  2. Detection:
    • Sprint burndown charts (GitHub Projects)
    • Weekly sprint reviews (adjust estimates)
  3. Response:
    • If 1 week behind: Work weekends (time-and-a-half pay)
    • If 2+ weeks behind: Reduce scope (defer Judge Arm mock to Phase 2)
    • If >3 weeks behind: Re-plan Phase 1, split into Phase 1a and 1b

Contingency Plan:

  • Scope Reduction Options:
    1. Defer Reflex Layer L1 cache (use Redis only)
    2. Defer Executor Python script handler (shell only)
    3. Reduce E2E test scenarios (5 → 3)
    4. Defer demo video (create in Phase 2)
  • Budget Impact: +$10k-20k if 2-3 week delay

Owner: Tech Lead Review Frequency: Weekly

Medium Risks

RISK-005: Database Connection Pool Exhaustion

Category: Technical Probability: MEDIUM (30%) Impact: MEDIUM (5/10) Risk Score: 1.5/10

Description: Orchestrator exhausts PostgreSQL connections under load, causing request failures.

Mitigation:

  • Tune pool size (10-20 connections)
  • Add connection timeout (5s)
  • Implement circuit breaker
  • Load test with 100 concurrent tasks

Contingency: Increase pool size or add read replicas

Owner: Python Engineer (Senior)

RISK-006: LLM API Rate Limits

Category: External Dependency Probability: MEDIUM (35%) Impact: LOW (3/10) Risk Score: 1.05/10

Description: OpenAI/Anthropic rate limits hit during testing or production.

Mitigation:

  • Use mocks for most tests
  • Exponential backoff retry logic (3 retries, 1s/2s/4s delays)
  • Fallback to Anthropic if OpenAI limited
  • Request rate limit increase from OpenAI ($100/month min spend)

Contingency: Implement request queue with controlled rate

Owner: Python Engineer (Senior)

RISK-007: Docker Daemon Failure

Category: Infrastructure Probability: LOW (10%) Impact: HIGH (7/10) Risk Score: 0.7/10

Description: Docker daemon crashes, making Executor Arm unavailable.

Mitigation:

  • Health checks with automatic restart
  • Circuit breaker (disable Executor if unhealthy)
  • Graceful degradation (return error, don't crash system)

Contingency: Manual docker restart, escalate to DevOps

Owner: DevOps Engineer

RISK-008: Integration Test Flakiness

Category: Quality Probability: HIGH (70%) Impact: LOW (2/10) Risk Score: 1.4/10

Description: E2E tests fail intermittently due to race conditions, timing issues.

Mitigation:

  • Proper service startup waits (health check polling)
  • Isolated test data (UUID prefixes)
  • Teardown after each test
  • Retry failed tests once (pytest --reruns=1)

Contingency: Disable flaky tests temporarily, fix in Phase 2

Owner: QA Engineer

RISK-009: Team Member Unavailability

Category: Resource Probability: MEDIUM (40%) Impact: MEDIUM (4/10) Risk Score: 1.6/10

Description: Key team member (Rust Engineer) sick or leaves during Phase 1.

Mitigation:

  • Documentation (README, inline comments, ADRs)
  • Knowledge sharing (pair programming, code reviews)
  • Cross-training (QA learns Rust basics)

Contingency: Hire contractor ($200/h) or extend timeline

Owner: Tech Lead

Low Risks

(12 additional low-priority risks documented but not detailed here)

  • Redis connection failures
  • PostgreSQL schema migration issues
  • Git merge conflicts
  • CI/CD pipeline failures
  • LLM API pricing changes
  • IDE license expiration
  • Network outages
  • Hard drive failures
  • Code review delays
  • Scope creep
  • Unclear requirements
  • Inadequate testing

Risk Monitoring & Review

Weekly Risk Review (Fridays, 30 minutes)

Agenda:

  1. Review risk register (5 min)
  2. Update risk probabilities/impacts based on week's progress (10 min)
  3. Identify new risks from past week (5 min)
  4. Adjust mitigation plans (5 min)
  5. Escalate critical risks to stakeholders (5 min)

Attendees: Tech Lead, all engineers

Output: Updated risk register, action items

Risk Escalation Criteria

Escalate to Stakeholders If:

  • Any critical risk probability increases above 20%
  • Any high risk impacts Phase 1 completion date
  • Budget overrun >10% ($7,750)
  • Security vulnerability found (critical/high severity)

Escalation Path:

  1. Tech Lead → Engineering Manager (Slack, <4 hours)
  2. Engineering Manager → CTO (Email + meeting, same day)
  3. CTO → Executive Team (if budget/timeline impact >20%)

Contingency Budget

Labor Buffer: 80 hours ($12,000) LLM API Buffer: $50 Cloud Infrastructure Buffer: $100 (if using GCP) Security Audit Budget: $5,000 (if needed)

Total Contingency: $17,150 (22% of base budget)

Burn Rate Threshold: If >50% of buffer used before Week 6, escalate to stakeholders

Appendices

Appendix A: Risk Scoring Matrix

ProbabilityImpact Low (1-3)Impact Medium (4-6)Impact High (7-10)
High (60-90%)1.5-2.7 (Medium)2.4-5.4 (High)4.2-9.0 (Critical)
Medium (30-60%)0.9-1.8 (Low)1.2-3.6 (Medium)2.1-6.0 (High)
Low (5-30%)0.05-0.9 (Low)0.2-1.8 (Low)0.35-3.0 (Medium)

Appendix B: Risk Response Strategies

  • Avoid: Eliminate risk by changing approach
  • Mitigate: Reduce probability or impact
  • Transfer: Outsource (insurance, third-party)
  • Accept: Acknowledge risk, no action

Document Version: 1.0 Last Updated: 2025-11-12 Next Review: Week 1 Friday Owner: Tech Lead Approvers: Engineering Manager, CTO