Phase 1: Resource Planning & Requirements

Version: 1.0 Date: 2025-11-12 Phase: Phase 1 - Proof of Concept Duration: 8.5 weeks Total Hours: 340 hours

Team Composition

Required Roles & FTE Allocation

Note: 500h total includes 160h buffer for:

• Code reviews (10% overhead)
• Team meetings (5% overhead)
• Documentation (5% overhead)
• Unexpected blockers (10% overhead)

Team Structure

Reporting Structure:

Phase 1 Tech Lead (Rust Engineer)
├── Rust Engineer (Reflex + Executor)
├── Python Engineer Senior (Orchestrator + Planner)
│   └── Python Engineer Mid (Orchestrator support)
├── DevOps Engineer (Integration)
└── QA Engineer (Testing)
    └── Security Engineer (Sprint 1.4 only)

Communication:

• Daily standups: 15min async (Slack)
• Weekly sprint reviews: 1h (Fridays)
• Bi-weekly architecture reviews: 1h
• Ad-hoc pair programming: as needed

Skill Requirements

Must-Have Technical Skills

Backend Development

• Python 3.11+: async/await, type hints, Pydantic, FastAPI
• Rust 1.82.0: ownership model, lifetimes, async/tokio, error handling
• REST API Design: HTTP methods, status codes, versioning, pagination
• Database Design: PostgreSQL schema, indexes, queries, connection pooling
• Caching: Redis data structures, TTL, eviction policies

Infrastructure & DevOps

• Docker: Dockerfile, docker-compose, networking, volumes, health checks
• Git: Branching strategies, PRs, conflict resolution, commit hygiene
• CI/CD: GitHub Actions, automated testing, linting, security scans
• Observability: Prometheus metrics, structured logging, distributed tracing

Testing

• Python Testing: pytest, pytest-cov, pytest-asyncio, mocking
• Rust Testing: cargo test, cargo tarpaulin, integration tests
• Load Testing: k6, Locust, JMeter
• Security Testing: OWASP Top 10, container security, penetration testing

Nice-to-Have Skills

• LLM Frameworks: LangChain, LlamaIndex, guidance
• Prompt Engineering: OpenAI/Anthropic best practices, token optimization
• Kubernetes: For Phase 2 prep (not required for Phase 1)
• Vector Databases: Qdrant, Weaviate (Phase 2)
• ML/Data Engineering: Embeddings, semantic search (Phase 2)

Skill Matrix by Role

Legend: Expert (can teach others), Advanced (can work independently), Basic (can contribute with guidance)

Onboarding Plan

Pre-Start (Week -1)

IT Setup (DevOps responsibility):

• Provision GitHub access (add to OctoLLM-dev team)
• Create LLM API accounts:
  • OpenAI organization, generate API key (budget: $500/month)
  • Anthropic workspace, generate API key (budget: $300/month)
• Set up Slack channels:
  • #octollm-dev (general development)
  • #octollm-alerts (CI/CD, monitoring)
  • #octollm-standup (daily updates)
• Grant GCP access (if using cloud for testing)
• Send welcome email with onboarding checklist

Individual Setup (Each engineer):

• Install development tools:
  • Docker Desktop / Podman (latest stable)
  • Python 3.11+ (via pyenv: pyenv install 3.11.6)
  • Rust 1.82.0 (via rustup: rustup install 1.82.0)
  • IDE: VS Code + extensions (Rust Analyzer, Python, Docker)
• Clone repository: git clone https://github.com/your-org/OctoLLM.git
• Install pre-commit hooks: pre-commit install
• Verify environment: make test-env (runs health checks)
• Review documentation:
  • CLAUDE.md (15 minutes)
  • docs/README.md (30 minutes)
  • ref-docs/OctoLLM-Project-Overview.md (1 hour)
  • ref-docs/OctoLLM-Architecture-Implementation.md (2 hours)

Week 1: Kickoff & Ramp-Up

Day 1: Team Kickoff (3 hours total):

• 09:00-10:30: Architecture deep dive (Tech Lead presentation)
  • System overview (5 layers, 4 components)
  • Biological inspiration (octopus neurobiology)
  • Phase 1 goals and success criteria
  • Sprint breakdown (1.1-1.5)
• 10:45-11:30: Codebase tour (live demo)
  • Repository structure walk-through
  • Documentation organization
  • CI/CD pipeline explanation
  • Development workflow (feature branches, PRs, code review)
• 11:30-12:00: Q&A and team introductions

Day 2-3: Environment Setup & First Tasks:

• Set up local development environment (Python venv, Rust toolchain)
• Run existing tests: make test (should pass from Phase 0)
• Complete first task:
  • Rust Engineer: Set up Reflex Layer project structure (Sprint 1.1.1)
  • Python Senior: Set up Orchestrator project structure (Sprint 1.2.1)
  • Python Mid: Set up database schema review (Sprint 1.2.3)
  • DevOps: Review CI/CD pipelines, plan Docker Compose structure
  • QA: Set up test frameworks, review testing strategy
• Submit first PR (even if WIP) to validate workflow

Day 4-5: Sprint 1.1 Kickoff:

• Sprint planning meeting (1 hour): detailed task breakdown
• Assign sprint tasks (Rust Engineer + QA focus on Sprint 1.1)
• Begin implementation work
• First daily standup (establish rhythm)

Ongoing Onboarding (Weeks 2-4)

Weekly 1-on-1s (Tech Lead with each engineer):

• Check-in on progress, blockers, questions
• Review code quality and best practices
• Career development discussion (15 min)

Bi-Weekly Architecture Reviews (Entire team):

• Review design decisions made during sprint
• Document Architecture Decision Records (ADRs)
• Discuss trade-offs and alternatives considered

Mentorship & Pair Programming:

• Rust Engineer pairs with Security Engineer (Sprint 1.4)
• Python Senior mentors Python Mid (Sprint 1.2)
• QA Engineer shadows developers for test coverage

Infrastructure Requirements

Local Development Environment

Hardware Requirements (Per Engineer)

Software Requirements

Operating System:

• macOS 12+ (Monterey or later)
• Ubuntu 22.04 LTS or later
• Windows 11 with WSL2 (Ubuntu 22.04)

Development Tools:

# Python
pyenv 2.3+
python 3.11.6
pip 23.0+
poetry 1.6+ (optional, or pip-tools)

# Rust
rustup 1.26+
rustc 1.82.0
cargo 1.82.0

# Docker
docker 24.0+
docker-compose 2.20+

# Database Clients
psql (PostgreSQL 15+ client)
redis-cli (Redis 7+ client)

# IDE (choose one)
VS Code 1.85+ with extensions:
  - Rust Analyzer
  - Python (Microsoft)
  - Docker
  - GitLens
  - Prettier
PyCharm Professional 2023.3+ (Python focus)
RustRover 2023.3+ (Rust focus)

# Version Control
git 2.40+
gh (GitHub CLI) 2.40+ (optional)

# Optional (nice to have)
k9s (Kubernetes TUI, for Phase 2 prep)
httpie / curl (API testing)
jq (JSON processing)

Shared Services & Accounts

LLM API Accounts

OpenAI (Primary):

• Organization: "OctoLLM Development"
• Billing: Pay-as-you-go
• Budget Alert: $500/month hard limit
• API Keys: 1 per environment (dev, staging)
• Models:
  • GPT-4-Turbo (orchestrator fallback)
  • GPT-3.5-Turbo-1106 (planner, cheaper)
• Estimated Cost: ~$75 for Phase 1

Anthropic (Fallback):

• Workspace: "OctoLLM Development"
• Billing: Pay-as-you-go
• Budget Alert: $300/month hard limit
• API Keys: 1 per environment
• Models:
  • Claude 3 Opus (high-quality fallback)
  • Claude 3 Sonnet (medium-quality, faster)
• Estimated Cost: ~$25 for Phase 1

CI/CD (GitHub Actions)

Current Usage (from Phase 0):

• Lint workflow (Python: ruff, black / Rust: clippy, fmt)
• Test workflow (pytest, cargo test)
• Security scan workflow (bandit, safety, trivy, gitleaks)
• Build workflow (Docker image builds)

Phase 1 Additions:

• Integration test workflow (docker-compose up, pytest e2e)
• Performance benchmark workflow (k6 load tests)
• Documentation deploy workflow (mkdocs to GitHub Pages)

Free Tier Limits:

• 2,000 minutes/month (Linux runners)
• 500MB artifact storage
• Estimated Phase 1 usage: ~1,000 minutes/month (within limits)

Monitoring & Observability (Optional)

Local Development (Docker Compose):

• Prometheus (metrics scraping)
• Grafana (dashboard visualization)
• Loki (log aggregation)
• Jaeger (distributed tracing)

Note: Monitoring stack runs locally in Docker Compose. No cloud costs.

Cloud Resources (Optional for Phase 1)

Primary Strategy: Local Docker Compose deployment (no cloud required)

Optional GCP Resources (if team prefers cloud testing):

Recommendation: Defer cloud resources to Phase 2. Use local Docker Compose for Phase 1 to minimize costs.

Budget Breakdown

Labor Costs

Blended Hourly Rates (Industry averages for San Francisco Bay Area):

Total Labor Cost Calculation:

Blended Rate: $154.80/hour

Infrastructure Costs

LLM APIs (Development & Testing):

• OpenAI: ~$75 (1.75M tokens, mostly GPT-3.5)
• Anthropic: ~$25 (150 fallback tests)
• Total LLM: ~$100

CI/CD:

• GitHub Actions: $0 (within free tier)

Cloud Resources (Optional):

• GCP: $0 (using local Docker Compose)
• Alternative if using cloud: ~$195/month × 2 months = ~$390

Development Tools:

• IDEs: $0 (VS Code free, or existing PyCharm/RustRover licenses)
• Docker Desktop: $0 (free for developers)

Total Infrastructure: ~$100 (LLM APIs only)

Grand Total Phase 1 Budget

Alternative (if using GCP): $77,790

Cost per Deliverable:

• Reflex Layer: $14,400 (Sprint 1.1: 80h × $180/h)
• Orchestrator MVP: $15,600 (Sprint 1.2: 80h blended)
• Planner Arm: $10,800 (Sprint 1.3: 60h blended)
• Executor Arm: $16,200 (Sprint 1.4: 80h blended, includes security)
• Integration & E2E: $6,000 (Sprint 1.5: 40h blended)
• Total: $63,000 (direct sprint hours)
• Overhead: $14,400 (code reviews, meetings, buffer)
• LLM APIs: $100

Timeline & Availability

Sprint Schedule

Note: Sprints 1.1 and 1.2 overlap (weeks 2-3) with different engineers working in parallel.

Team Availability Assumptions

• Full-time: Rust Engineer, Python Senior, QA Engineer
• Part-time (50%): DevOps Engineer (20h/week), Python Mid (20h/week), Security Engineer (20h/week in Sprint 1.4 only)
• Holidays/PTO: 10% buffer built into 500h estimate (50h buffer)
• Meetings: 5% overhead (25h total across 8.5 weeks)

Critical Path Analysis

Longest Dependency Chain:

1. Sprint 1.1 (Reflex Layer): Week 1-2 (no dependencies)
2. Sprint 1.2 (Orchestrator): Week 2-4 (can use reflex or direct pass-through)
3. Sprint 1.3 (Planner): Week 4-5.5 (can develop in parallel, orchestrator can fallback to direct LLM)
4. Sprint 1.4 (Executor): Week 5.5-7.5 (depends on orchestrator for routing)
5. Sprint 1.5 (Integration): Week 7.5-8.5 (depends on all 4 components)

Parallel Work Opportunities:

• Weeks 2-3: Reflex Layer finalization + Orchestrator initial development
• Weeks 4-5: Planner development + Orchestrator finalization (can run in parallel)

Critical Path Total: 6.5 weeks (1.1 + partial 1.2 + 1.3 + 1.4 + 1.5)

Scaling Plan (Phase 1 → Phase 2)

Team Growth

Phase 1: 4.5 FTE Phase 2: 5-6 FTE (add 1-2 engineers)

New Roles for Phase 2:

• ML/Data Engineer (1.0 FTE): Embeddings, semantic search, Qdrant integration
• Python Engineer (Additional) (0.5-1.0 FTE): Build Retriever, Coder, Judge, Guardian arms

Retention Strategy:

• Promote top performer from Phase 1 to Tech Lead for Phase 2
• Offer learning opportunities (Kubernetes, ML, embeddings)
• Maintain team continuity (avoid turnover between phases)

Infrastructure Scaling

Phase 1: Local Docker Compose Phase 2: Kubernetes (GKE) + Cloud SQL + Memorystore + Qdrant

Transition Plan (1 week, Week 9):

• Migrate Docker Compose services to Kubernetes manifests
• Provision GCP resources (GKE cluster, Cloud SQL, Memorystore)
• Set up Helm charts or Kustomize
• Deploy Phase 1 components to Kubernetes (smoke test)
• Begin Phase 2 Sprint 2.1 (Week 10)

Appendices

Appendix A: Onboarding Checklist

IT Setup (DevOps):

• GitHub access granted (OctoLLM-dev team)
• OpenAI API key generated ($500/month limit)
• Anthropic API key generated ($300/month limit)
• Slack channels created (#octollm-dev, #octollm-alerts, #octollm-standup)
• GCP access granted (optional, if using cloud)
• Welcome email sent with onboarding docs

Individual Setup (Each Engineer):

• Docker Desktop installed and running
• Python 3.11.6 installed (pyenv)
• Rust 1.82.0 installed (rustup)
• IDE set up (VS Code + extensions or PyCharm/RustRover)
• Repository cloned and pre-commit hooks installed
• Environment verified (make test-env passes)
• Documentation reviewed (4 hours)
• Attended team kickoff meeting
• Completed first task and submitted PR

Appendix B: Communication Protocols

Daily Standups (Async, Slack #octollm-standup):

• Post by 10 AM local time
• Format: Yesterday / Today / Blockers
• Example: "Yesterday: Implemented PII detection module. Today: Adding unit tests. Blockers: Need regex test dataset."

Weekly Sprint Reviews (Fridays, 1 hour, Zoom):

• Demo completed work (live code demo)
• Review sprint metrics (velocity, test coverage, blockers)
• Plan next sprint tasks

Code Reviews (GitHub PRs):

• All code requires 1 approval before merge
• Reviewers assigned automatically (CODEOWNERS file)
• Response time SLA: 24 hours
• Use PR templates (checklist for tests, docs, changelog)

Incident Response:

• Critical bugs: Slack @channel alert, immediate response
• Non-critical bugs: GitHub issue, triage in weekly review
• Escalation path: Engineer → Tech Lead → Stakeholders

Appendix C: Tooling & Licenses

Free/Open Source:

• Docker Desktop (free for developers)
• VS Code (free)
• Git (free)
• Python (free)
• Rust (free)
• PostgreSQL (free)
• Redis (free)

Paid (Optional):

• PyCharm Professional: $249/year per developer (optional, can use VS Code)
• RustRover: $249/year per developer (optional, can use VS Code)
• GitHub Team: Included in organization plan

LLM APIs:

• OpenAI: Pay-as-you-go ($500/month budget)
• Anthropic: Pay-as-you-go ($300/month budget)

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