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VISION.md

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Deckent — Vision and Strategy


Vision

Deckent is building toward a fully autonomous AI development assistant — in the same category as OpenClaw, Microsoft Copilot Cowork, and Devin. The current phase is AI agent orchestration: a multi-agent CLI that plans, executes, and evaluates development tasks in parallel sprints. This is not the destination — it is the foundation.

The long-term goal: Deckent becomes an always-on, self-improving development teammate. It understands your codebase, learns from every sprint, plans proactively, and operates with minimal human input. Open source, self-hosted, provider-agnostic — the anti-Devin.


Mission

Solo AI assistants are inherently limited: one context window, one task, one perspective. Deckent breaks through this ceiling with its Brain-Worker-Auditor architecture. Brain sets the strategy, Workers execute in parallel, Auditor guarantees quality. After every sprint, learnings persist to memory — the system makes better decisions with each iteration.

Where we are now: AI orchestration CLI — sprint-based multi-agent execution with 3 spawn backends (tmux, subprocess, Docker), 3 AI providers, 16 agents, 21 skills.

Where we are going: Autonomous AI assistant — heartbeat daemon, proactive task execution, channel integrations (Slack, GitHub), codebase semantic understanding, always-on gateway. Think OpenClaw's architecture + Deckent's multi-agent discipline.


Target Users

Segment Profile Deckent Value
Individual developer Indie dev, freelancer, solo founder Multi-agent power for a one-person team — parallel output through sprints
Small team 2-10 person startup or squad Use AI workers as team members — automate repetitive tasks
Enterprise Large-scale organization (future) Controlled autonomous development — audit trail, scope enforcement, memory/learning

Competitive Analysis

Tool Category Strength Weakness Deckent Position
OpenClaw Autonomous AI assistant (343K+ stars) Always-on daemon, 13K+ skills, 50+ channels Single agent, no sprint lifecycle, no scope enforcement Multi-agent orchestration + sprint discipline + learning
Copilot Cowork Enterprise AI orchestrator Multi-model critique layer, M365 integration Closed source, $30+/user/month, no self-hosted Open source, self-hosted, free, provider-agnostic
Devin Autonomous software engineer End-to-end autonomous, interactive planning Single agent, closed source, $20-500/month Multi-agent parallel, open source, free
Perplexity Computer Multi-model AI agent 19 models, days-long tasks, 400+ apps $200-325/month, no self-hosted, no sprint planning Self-hosted, 13 models, sprint-based structure
Claude Code (solo) Single AI assistant Strong single-task performance Single context, no parallelism Uses Claude Code as a worker, adds orchestration

Deckent's unique position: The only open-source tool that combines multi-agent parallel execution + sprint lifecycle + scope enforcement + memory/learning + multi-provider support + self-hosted. Current phase: orchestration CLI. Next phase: autonomous assistant (OpenClaw/Cowork class).


Technology Decisions

TypeScript + ESM

TypeScript delivers type safety that enables confident refactoring across large codebases. ESM (ES Modules) aligns with the modern Node.js ecosystem and unlocks optimizations like tree-shaking. For a system that manages AI agents, type safety is critical — a malformed config or task structure can crash an entire sprint.

Multi-Provider (Claude + Codex + Gemini)

Depending on a single AI provider creates both cost and availability risk. Deckent's provider-agnostic architecture assigns different models to different tasks: opus for complex architectural decisions, haiku for simple documentation. A provider fallback chain ensures resilience against outages.

Triple Spawn Backend (tmux + Subprocess + Docker)

Three backends for different contexts: tmux (fastest, live terminal, Linux/macOS default), subprocess (Windows fallback, file-based tracking), Docker (container isolation, resource limits, CI/CD ready). Each worker runs in its own isolated environment regardless of backend.

MCP (Model Context Protocol) Integration

MCP integrates Deckent with any MCP-compatible IDE or tool. With 21 tools and 8 resources, the entire sprint lifecycle is programmatically accessible. This makes Deckent not just a CLI, but a platform.

Docker Container Isolation

Workers run in isolated Docker containers with memory limits, non-root execution, and volume-mounted auth. Project filesystem access is controlled per-container. This is the foundation for enterprise deployment, CI/CD integration, and future Kubernetes scaling.


Roadmap

Phase 1: "Orchestration Foundation" — Complete (Sprint 1-82)

Core sprint lifecycle, multi-agent parallel execution, tmux/subprocess backends, MCP integration, multi-provider support (Claude + Codex + Gemini), ModelRegistry, agent/skill ecosystem, heartbeat daemon, human checkpoints, adaptive thresholds.

Phase 2: "Beta Readiness" — Active (Sprint 83-130)

Docker container backend (live-verified Sprint 119-129, 10 e2e tests, configurable timeout), documentation consolidation (BETA-TRACKER, i18n generators, docs.json automation), ERRORS.md active logging, backend smoke testing (tmux + subprocess + Docker via MCP + CLI), dashboard backend badge, ADR-027 hybrid backend decision, version 0.4.0-beta.1. Sprint 130: MCP instructions accuracy fix (21 tools), decision-engine V1 @deprecated archive + ADR-028, real coverage measurement (89.33%).

Phase 3: "Public Beta" — Next

VerhexIO/deckent open-source repository, CI/CD pipeline (GitHub Actions + Docker backend), npm publish, .detect-secrets, CONTRIBUTING guide, community onboarding.

Phase 4: "Autonomous Assistant" — Future

The leap from orchestration CLI to autonomous AI assistant:

  • Always-on gateway — daemon mode, SSE dashboard, remote control
  • Channel integrations — Slack bot, GitHub Issues/PR automation, Linear/Jira sync
  • Codebase semantic understanding — AST indexing, dependency graph, RAG-enhanced context
  • Multi-sprint chaining — days-long autonomous task execution
  • Critique layer — multi-model verification (writer + reviewer pattern)
  • Browser/Computer Use — Claude Computer Use SDK integration
  • Provider expansion — Grok, Llama, Mistral, DeepSeek (ModelRegistry ready)

This is where Deckent enters the OpenClaw/Cowork/Devin category — not as another single-agent tool, but as the only open-source multi-agent autonomous development platform.


Values

  • Open source — Deckent is free and open source. Community contributions are welcome.
  • Transparency — Every sprint's plan, outcome, and learnings are on record. The .brain/ directory preserves the full decision history.
  • Quality — Auditor quality gates, GO/NO-GO evaluation, and mandatory testing ensure every sprint meets quality standards.
  • Autonomous yet controlled — Deckent operates autonomously, but the user is always in control. Scope enforcement, audit trails, and memory budgets set clear boundaries.
  • Continuous learning — MEMORY.md and PATTERNS.md are updated after every sprint. The system improves its decisions over time and avoids repeating the same mistakes.
  • Orchestration first, autonomy next — Deckent starts as a sprint-based orchestrator and evolves toward full autonomy. Each phase builds on the previous — no shortcuts, no half measures.

Deckent by the Numbers

Metric Value
Version 1.0.0-beta.1
Sprint sprint-162a (T4 hot fix)
MCP Tools 29 (verified)
MCP Resources 8
CLI Commands 48 (verified)
Dashboard Pages 6-7
Agents 18 (15 built-in + 3 custom)
Skills 21 built-in
Providers 3 (Claude, Codex, Gemini)
ADRs 47 (44 accepted + ADR-047 NEW multi-lang)
Multi-language stacks 6 baseline (TS/Py/Go/Rust/Java/C#)

Sprint History

Sprint Status Notable
sprint-161 FAILED (mid-flight orchestration broke) God-audit produced 356 findings, 8 bugs identified
sprint-162-failed-build-race FAILED (build-after-spawn race) dist mtime > sprint-state by 22s; Brain loaded old code
sprint-162a T4 IMPLEMENTATION ✓ 8 bug closed + multi-lang adapter + 4 ADR + 12-lang i18n + ARIA + security review
sprint-162-live PARTIAL SUCCESS 8/8 known bugs LIVE proven; Bug X discovered (dual-evaluator stale-state path race); org quota cascade at 15:36Z

Sprint Metrics

Metric Value
Sprint sprint-162a
Status PARTIAL — 8/8 bug closed + Bug X discovered
Total deliverables 8 fix-spec + 14 source + 14 test + 4 ADR + 12-lang i18n + ARIA + security
Tests added ~305
Source LoC +2703/-801 (69 files)
Beta GA blocker Bug X cost cascade (Sprint 162B P0)