# AURORA-SE Production Readiness Plan

## Vision

Deliver **Aurora-SE** as a production-grade autonomous engineer that developers can install via `pip install aurora-se` (with an optional Node.js wrapper), connect to either cloud APIs or local LLMs, and rely on for end-to-end PDCA (Plan → Do → Check → Act) automation across modern software projects. The system must compete with—and exceed—the polish of offerings from major vendors (Claude Code, Gemini CLI, etc.), while preserving Aurora’s differentiators: deep telemetry, governance, and self-learning.

## Guiding Principles

- **Security-first**: Harden sandboxes, secrets, policies, and supply chain before advanced features.
- **Deterministic & Observable**: Every action emits auditable telemetry, metrics, and governance artifacts.
- **Composable**: Pluggable models, CI steps, reward signals, and tools via a formal extension SDK.
- **User Experience Matters**: CLI/REPL, automation loops, and dashboards must feel first-class.
- **Test Exhaustively**: Unit, integration, end-to-end (including long-running autopilot flows) and security/chaos tests gate each release.
- **Docs & Support**: Invest in runbooks, templates, and onboarding for individuals, teams, and enterprises.

## Cross-Cutting Requirements

- Automated **CI pipeline** running linting, typing, unit tests, integration suites, fuzz/chaos/security checks, and packaging verification on every PR.
- **Secrets & Policy Enforcement** baked into executors and federation flows.
- **Telemetry stack** (Prometheus, OTEL, PDCA) must support multi-tenant isolation and retention policies.
- **Release engineering**: signed artifacts, SBOM generation, vulnerability scanning, and reproducible builds.

## Phase Overview

| Phase | Theme | Key Outcomes |
|-------|-------|--------------|
| 0 | Comprehensive Audit & Foundations | Baseline discoveries, architectural decisions, backlog shaping, design approvals |
| 1 | Sandbox & Security Hardening | Production isolation, secret scanning, policy enforcement |
| 2 | Planner & Model Orchestration | Robust model routing, retries, local LLM support, redaction |
| 3 | Executor & CI Productionization | Resilient patching, CI pipelines, rollback, diff explainers |
| 4 | Reward Engine & Experience Vault | Real metric ingestion, adaptive schedulers, explainability |
| 5 | Learning & Federation | Real LoRA/PEFT, adapter registry, secure federation |
| 6 | Evaluation & Benchmarking | SWE-Bench + internal suites, analytics, trend dashboards |
| 7 | CLI/UX & Extension Ecosystem | REPL, sessions, autopilot, plugin SDK, workspace management |
| 8 | Telemetry, Governance, Compliance | Live dashboards, governance bundles, audit tooling |
| 9 | Packaging, Docs, Launch Ops | Installers, documentation, support readiness, GTM |

Each phase ends only when all exit criteria (deliverables + verification) are satisfied and reviewed.

---

## Phase 0 – Audit & Foundations

**Objectives**

- Inventory all subsystems, identify placeholders, dependencies, and security gaps.
- Define production architecture diagrams, data flow, threat model, and privacy considerations.
- Establish engineering standards (coding, testing, observability, incident response).

**Key Tasks**

- Conduct architecture review sessions covering planner, executor, reward, telemetry, governance.
- Produce threat model & STRIDE analysis for sandbox, federation, telemetry, package distribution.
- Catalog current tests; define required unit/integration/E2E coverage goals.
- Finalize technology choices for Firecracker, LoRA frameworks, telemetry stack, package tooling.
- Build detailed backlog & timelines for phases 1–9 with resource estimates.

**Immediate Remediation Items Identified (Phase 0 priority)**

- `aurora/planner/service.py`: initialize `_config` before constructing `ExperienceVault`, and pass the resulting vault into `build_context_packer` so experience retrieval works.
- `aurora/planner/client.py`: import `asyncio` and `os` to unblock retry logic and critic authentication.
- `aurora/indexer/context.py` & stores: add `kind` support to `GraphStore.neighbors` and expose an `iter_recent` adapter on the experience vault to stop context assembly crashes.
- `aurora/executor/policy.py`: parse policy files as YAML (or ship JSON) so the default `policies/security.yaml` loads successfully.
- `cli/main.py`: import `FirecrackerSandbox` so the CLI respects firecracker profiles without crashing.
- `configs/ci_profiles.yaml`: replace nonexistent commands/assets (`npm run lint`, `configs/perf/*.js`, `scripts/run_fuzz.py`) with working equivalents and ensure required scripts exist.

**Deliverables**

- Architecture & threat-model documentation in `docs/architecture/`.
- Updated `docs/phase_plan.md` crosslinked to this `plan.md`.
- Backlog issues / tickets referencing phase tasks with priority & owner.
- CI pipeline scaffolding (lint, type-check, unit tests) enforced on PR.

**Verification & Exit Criteria**

- Security sign-off on threat model.
- CI green on baseline suite (current pytest set) + static analysis (ruff/mypy/bandit).
- Stakeholder approval of backlog & timeline.

---

## Phase 1 – Sandbox & Security Hardening

**Objectives**

- Replace placeholder sandbox implementations with production-grade isolation.
- Integrate comprehensive secret scanning, policy enforcement, and audit logging.

**Key Tasks**

- Implement Firecracker/MicroVM sandbox with network/egress policies, resource quotas, snapshot management.
- Provide Docker sandbox parity with seccomp/apparmor profiles and read-only mounts.
- Expand secret scanning (TruffleHog, GitLeaks) and integrate allow-lists.
- Implement policy engine with license allow-list, SBOM gating, CVE thresholds.
- Harden sandbox startup/teardown, failure recovery, and caching of dependencies.

**Deliverables**

- `aurora.executor.sandbox.FirecrackerSandbox` production implementation + configuration docs.
- Policy DSL in `policies/` with enforcement in executor & federation flows.
- Automated SBOM generation + security scan scripts in CI.
- Audit logging integrated with `telemetry/errors.jsonl` and governance bundles.

**Phase 1 Hardening Summary**

- Docker runtime now enforces read-only root, curated mounts, seccomp/AppArmor profiles, and resource quotas.
- Firecracker sandbox launches disposable microVMs via `firectl`, respecting egress policies and snapshotting rootfs.
- Composite secret scanning (regex + TruffleHog/GitLeaks) with allow/deny lists protects workspaces.
- Security policy DSL gates SBOM, CVE, and license reports generated in CI.
- CI workflows emit SBOM/CVE/license artifacts and archive chaos test outcomes for governance.

**Verification & Exit Criteria**

- Security pen-test on sandbox surfaces.
- Chaos tests: abrupt sandbox termination, network denial, resource starvation.
- Integration tests: executor applying edits under Firecracker and Docker with policy rules enforced.
- SBOM + CVE scan gating release pipeline.

---

## Phase 2 – Planner & Model Orchestration

**Objectives**

- Make the planner resilient, multi-model aware, and privacy-safe.
- Support both cloud API and local LLM integrations with dynamic routing.

**Key Tasks**

- Implement model routing abstraction supporting Anthropic, OpenAI, Google, Ollama/local weights.
- Add circuit breakers, retries with exponential backoff, timeout budgets, streaming support.
- Expand secret redaction (regex + ML-based) and outbound data minimization.
- Build critic stack orchestration with configurable consensus/priority rules.
- Persist planner sessions, context slices, and critic outcomes for replay.

**Deliverables**

- Planner config supporting multiple providers, failover rules, cost tracking.
- Local model integration playbooks (Ollama, llama.cpp, vLLM) with benchmarking harness.
- Enhanced self-edit schema to include risk levels, remediation scripts, policy notes.
- Planner regression suite hitting mocked APIs and local LLM harness.

**Verification & Exit Criteria**

- Load tests simulating concurrent plan requests (target latency SLOs).
- Privacy review confirming no sensitive data egress without redaction.
- Unit + integration tests covering routing, retry behavior, critic orchestration.
- Benchmark report comparing planner against baseline tasks and competitor CLIs.

---

## Phase 3 – Executor & CI Productionization

**Objectives**

- Ensure executor handles complex edits, diff conflicts, rollbacks, and full CI pipelines.
- Provide rich diff explainers, manual approval hooks, and drift detection.

**Key Tasks**

- Implement patch conflict resolution with three-way merge support & fallback guidance.
- Extend CI orchestrator for parallel steps, dynamic timeouts, log streaming.
- Integrate coverage, fuzz, load, security tools (k6, Atheris/Jazzer, Syft, Grype, Semgrep).
- Add diff explainers and acceptance gates (complexity thresholds, test coverage deltas).
- Build manual approval workflow with interactive prompts or API callbacks.

**Deliverables**

- Enhanced `CIProfile` definitions with dependencies and retry policies.
- Diff summary artifacts (HTML/Markdown) stored in `artifacts/`.
- Drift detection (git status checks, untracked file guardrails) with corrective actions.
- Executor telemetry enriched with per-step metrics and correlation IDs.

**Phase 3 Productionization Summary**

- Patch application now supports three-way merge fallback with drift guardrails and diff explainers recorded under `artifacts/`.
- CI orchestrator captures per-step metadata (durations, retries, correlation IDs) with dependency-aware execution and sandbox log streaming.
- Manual approval workflow and policy gates integrate SBOM/CVE/license artifacts plus chaos/coverage outputs for governance.

**Verification & Exit Criteria**

- End-to-end autopilot run on demo repo + large OSS repo (>=100k files).
- CI passing time SLOs (≤15 min thorough profile, ≤5 min incremental index+fast CI).
- Regression suite for diff operations, rollback scenarios, secret/policy enforcement.
- Manual approval flow tested in headless + interactive contexts.

---

## Phase 4 – Reward Engine & Experience Vault

**Objectives**

- Replace heuristics with real metric ingestion, adaptive weighting, and explainability.
- Capture high-fidelity experience data for learning and planner conditioning.

**Key Tasks**

- Integrate CI/log parsers for coverage, latency, security, complexity metrics.
- Implement adaptive scheduler with configurable strategies (bandits, reinforcement signals).
- Provide explainability dashboards (JSON + HTML) describing reward components and confidence.
- Harden experience vault with dedupe, retention, and privacy controls.
- Expose reward API for external consumers (dashboards, downstream ML).

**Deliverables**

- Reward configuration schema with tunable weights, penalties, and schedules.
- Experience vault metadata indices (by task, reward, policy notes).
- Visualization artifacts under `artifacts/reward_reports/` with historical trends.
- Reward regression suite comparing expected vs actual reward under fixture CI outputs.

**Verification & Exit Criteria**

- Statistical validation across sample runs (reward stability, monotonicity for key metrics).
- Load tests on experience vault (import/export) with large datasets.
- Bias/fairness report ensuring penalties behave as designed.
- Compliance sign-off on retention and privacy policies.

---

## Phase 5 – Learning & Federation

**Objectives**

- Enable real adapter training (LoRA/PEFT) with hardware auto-detection and bias controls.
- Build secure federation for adapter sharing with policy enforcement.

**Key Tasks**

- Integrate Hugging Face Accelerate/PEFT, optional DeepSpeed for large models.
- Implement dataset curation pipeline (filtering, stratification, augmentation) based on experience logs.
- Build adapter registry with signing, provenance, changelog, validation scores.
- Develop federation sync with encryption, license enforcement, bias thresholds, audit trails.
- Provide CLI commands for training, evaluation, publishing, rollback of adapters.

**Deliverables**

- `aurora-se learn` upgrades: interactive mode, batch mode, nightly scheduling hooks.
- Adapter metadata spec (JSON Schema) + registry service.
- Federation policy pack with enforcement scripts and governance dashboard integration.
- Learning test harness with simulated + real training runs (smoke tests on CPU, full runs on GPU).

**Verification & Exit Criteria**

- Successful end-to-end training run on reference dataset with measurable reward uplift.
- Bias/ethics review of adapters; automated bias report gating promotion.
- Federation integration tests (happy path, policy violation, retry, audit export).
- Hardware detection verified on CPU-only, single GPU, multi-GPU environments.

---

## Phase 6 – Evaluation & Benchmarking

**Objectives**

- Operationalize SWE-Bench (Lite/Live/Full) and additional internal benchmarks.
- Produce analytics, dashboards, and correlations across planner/executor/reward versions.

**Key Tasks**

- Automate dataset management (download, cache, update), scenario scheduling (nightly, weekly, monthly).
- Implement evaluation artifact pipeline: raw results, metrics CSVs, compliance reports, telemetry snapshots.
- Build analytics dashboards (Plotly/Grafana) linking evaluation metrics to adapters, policy overrides, reward trends.
- Integrate evaluation outcomes into governance bundles and PDCA logs.
- Add post-processing for patch correctness, compile status, runtime metrics.

**Deliverables**

- Evaluation configuration templates for local + CI + staged environments.
- Dashboard at `docs/reports/weekly_dashboard.html` including trend lines, heatmaps, failure taxonomy.
- CLI commands for comparison (`aurora-se eval compare`, `aurora-se eval report`).
- Integration tests running sample SWE-Bench scenario inside sandbox.

**Verification & Exit Criteria**

- Reproducible evaluation runs with stable metrics (variance within agreed thresholds).
- Governance bundle includes latest evaluation insights and compliance notes.
- Alerting on evaluation failures or regressions (Prometheus rules / Slack webhooks).
- Stakeholder sign-off on evaluation reliability.

---

## Phase 7 – CLI/UX & Extension Ecosystem

**Objectives**

- Transform the CLI into a polished experience rivaling Claude/Gemini.
- Provide REPL, sessions, workspace snapshots, plugin marketplace, autopilot loop.

**Key Tasks**

- Implement `aurora-se shell` interactive REPL with streaming output, command shortcuts, `/context`, `/allow`, `/deny`.
- Add session management (`start`, `resume`, `archive`), workspace snapshots, reproducibility metadata.
- Build `autopilot` command orchestrating plan → apply → reward → eval with guardrails.
- Design plugin SDK (Python/Node) for external tools (context providers, CI steps, reward collectors).
- Add telemetry overlays (token/cost meter, run durations) and formatted outputs (JSON/Markdown).

**Complete CLI Command Surface**

**Top-Level Commands:**
- `aurora-se init` – Bootstrap workspace configuration
- `aurora-se index [--full|--incremental] [--root PATH] [--config PATH]` – Repository intelligence
- `aurora-se plan --task TEXT [--auto] [--critic] [--config PATH]` – Generate self-edit plans
- `aurora-se apply [--edit PATH] [--profile fast|balanced|thorough] [--ci-config PATH]` – Apply edits with CI
- `aurora-se reward [--compute] [--explain] [--config PATH]` – Manual reward operations (optional)
- `aurora-se learn [--nightly] [--federated] [--config PATH]` – Adapter training pipeline
- `aurora-se adapters [list|sync|publish|rollback|diff|verify] [--path PATH]` – Adapter management
- `aurora-se eval [run|compare|report] [--suite NAME] [--config PATH] [--export-metrics]` – Evaluation suite
- `aurora-se telemetry [init|dashboard|export] [--config PATH] [--export]` – Telemetry operations
- `aurora-se governance [bundle|policy|report] [--bundle PATH]` – Governance utilities
- `aurora-se shell` – Interactive REPL mode
- `aurora-se autopilot --task TEXT [--max-iterations N] [--max-cost USD] [--seed N]` – Autonomous loop
- `aurora-se session [start|resume|list|archive|delete] [NAME]` – Session management
- `aurora-se workspace [snapshot|restore|status] [TAG]` – Workspace state management
- `aurora-se plugin [create|list|install|remove|sign|publish] [NAME]` – Plugin ecosystem
- `aurora-se policy [check|set|list] [--profile NAME]` – Policy engine operations
- `aurora-se scripts run <script> [--args ...]` – Automation script wrapper
- `aurora-se report [weekly|monthly|governance|training] [--output PATH]` – Report generation
- `aurora-se model [list|switch|test] [NAME]` – Model management and routing
- `aurora-se notify [slack|email|webhook] [--config PATH]` – Notification configuration
- `aurora-se api [start|stop|status] [--port N]` – REST API server (optional)
- `aurora-se version` – Version information

**Interactive Shell Commands (`/` prefix):**
- `/model <name>` – Switch planner model profile
- `/critic on|off` – Toggle critic stack
- `/context` – Show current retrieval context slices
- `/allow <tool>` / `/deny <tool>` – Manage tool permissions
- `/session save|load <name>` – Manage sessions without leaving REPL
- `/workspace snapshot|restore` – Capture or revert repo state
- `/autopilot <task>` – Start autopilot run from within shell
- `/plugin list|enable|disable <name>` – Manage plugins live
- `/logs [--tail N]` – Tail recent telemetry
- `/reward [--explain]` – Show latest reward breakdown
- `/policy check` – Run policy validation
- `/help [command]` – Quick command reference
- `/quit` – Exit the shell

**Deliverables**

- CLI help docs & examples in `docs/cli/`.
- Plugin registry directory with signing and versioning.
- Autopilot workflow templates (policy approvals, human-in-the-loop options).
- UX design artifacts & usability testing reports.

**Verification & Exit Criteria**

- Usability tests with external developers (qualitative feedback, completion metrics).
- Automated CLI integration tests covering interactive and scripted flows.
- Plugin sample suite demonstrating context provider, CI step, reward hook.
- Documentation & tutorials published, including quickstart and advanced guides.

---

## Phase 8 – Telemetry, Governance, Compliance

**Objectives**

- Provide real-time observability, compliance automation, and audit-friendly reporting.
- Ensure telemetry supports multi-user isolation, retention policies, and export tooling.

**Key Tasks**

- Stand up metrics backend (Prometheus + Grafana), OTEL collector, log aggregation with correlation IDs.
- Build `aurora-se telemetry dashboard` command for local dashboards & remote links.
- Automate governance bundle creation (ethics charter, policy overrides, SWE-Bench trends, SBOM/CVE status).
- Implement compliance automation (SOC2-style checklists, incident response runbooks, SLA/SLO monitoring).
- Provide export utilities (`aurora-se telemetry export --interval`, `aurora-se governance report --weekly`).

**Deliverables**

- Telemetry configuration templates for local, staging, production.
- Governance documentation in `docs/governance/` with workflow diagrams.
- Incident response runbooks (`docs/runbooks/`) updated for production operations.
- Alerting rules + playbooks covering failures, policy breaches, bias warnings.

**Verification & Exit Criteria**

- Simulated incident drill covering detection, escalation, mitigation.
- Governance bundle reviewed by compliance stakeholders.
- Telemetry load tests (sustained runs, log volume, retention policy validation).
- SOC2/ISO-style gap analysis signed off.

---

## Phase 9 – Packaging, Documentation, Launch Operations

**Objectives**

- Package and distribute Aurora-SE for public consumption with enterprise-ready docs and support.
- Execute beta program, gather feedback, and launch.

**Key Tasks**

- Build Python package (`pip install aurora-se`), optional Node CLI wrapper (`npm i aurora-se-cli`).
- Implement installer scripts (virtualenv bootstrap, dependency checks, optional GPU detection).
- Produce comprehensive documentation site (mkdocs or Docusaurus) with tutorials, API reference, FAQ.
- Create sample projects, templates, and demo videos.
- Establish support processes (issue triage, community forums, SLA targets).
- Plan GTM: beta cohorts, feedback loops, release notes, versioning strategy (SemVer).

**Deliverables**

- Signed release artifacts, SBOMs, checksum manifests.
- Documentation portal and in-CLI help.
- Marketing/launch materials (blog, roadmap, comparison guides).
- Support/on-call rotations and SLAs documented in `docs/operations/`.

**Verification & Exit Criteria**

- Packaging validation on Windows/macOS/Linux (ARM & x86 where applicable).
- Beta program feedback addressed; launch readiness review passed.
- Monitoring & incident response dry-runs successful for launch day.
- v1.0.0 tag released with changelog, migration guide, and support plan.

---

## Post-Launch & Continuous Improvement

- Establish quarterly roadmap updates for advanced features (formal verification, multi-agent planners, enterprise integrations).
- Monitor telemetry for user behavior, failure patterns, and prioritize enhancements.
- Maintain public issue tracker, respond to CVEs, roll out hotfix processes.
- Expand partner ecosystem (IDE plugins, cloud integrations) and community contributions.

---

### Testing Matrix (Reference)

| Layer | Tools / Coverage |
|-------|------------------|
| Unit | pytest, hypothesis, coverage ≥90% on core modules |
| Integration | scenario-based tests for planner, executor, reward, learning, evaluation |
| End-to-End | Autopilot tasks on demo + large repos, long-running PDCA loops |
| Security | SAST (bandit, semgrep), DAST (OWASP ZAP), dependency scans (Syft/Grype), secrets scan |
| Performance | Load tests on planner, executor, telemetry, evaluation suites |
| Chaos/Resilience | Network faults, sandbox teardown, API outages, disk pressure |
| Compliance | Policy enforcement tests, governance bundle validation, audit trails |

### Documentation & Runbook Checklist

- Quickstart guides (<10 min setup), advanced configuration, model integration, sandbox troubleshooting.
- Runbooks for planner/executor failures, CI flakiness, reward anomalies, evaluation outages.
- Incident templates, escalation hierarchy, communication guidelines.
- Contribution guide, code of conduct, release process documentation.

---

## Next Steps

1. Secure stakeholder approval for this plan.
2. Spin up detailed backlog issues/milestones aligned with phases and assign owners.
3. Begin Phase 0 activities (audit, threat modeling, baseline CI) immediately.
4. Communicate roadmap to contributors and potential beta partners.

With disciplined execution across these phases, Aurora-SE can stand shoulder-to-shoulder with major industry offerings while maintaining its unique strengths in telemetry, governance, and autonomy.


