Swarm Lord

SwarmLord

"Root access for your cognitive workforce." An agentic control plane that connects LLM reasoning with deterministic system execution.

The Autonomous Orchestration Plane for Backend Engineering

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Executive Summary

Swarnlord is an experimental autonomous orchestration platform designed to bridge the gap between large language model (LLM) reasoning and real-world backend, infrastructure, and DevOps execution.

Unlike traditional coding assistants that act as passive text-generation tools, ARCHON functions as an Agentic Control Plane. It continuously observes system state, reasons about architecture and operational signals, and executes validated actions using a recursive OODA Loop (Observe → Orient → Decide → Act).

Built for AI-native backend engineering, not chat-based automation.

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Architectural Philosophy

SwarmLord follows a Hybrid Runtime Architecture that strictly separates high-level cognition from low-level execution.

This design ensures:

• Deterministic and auditable system behavior
• High throughput for IO-heavy workloads
• Strong safety boundaries for destructive operations

flowchart TB
    subgraph User
        U[Engineer / Operator]
    end

    subgraph ControlPlane["Control Plane (Node.js / TypeScript)"]
        CLI[Ink-based TUI]
        OODA[OODA Loop Engine]
        Planner[Planner Agent]
        Executor[Executor Agent]
        Reviewer[Reviewer Agent]
        MCP[MCP Negotiator]
        State[Persistent State & Memory]
    end

    subgraph ModelLayer["Model Layer"]
        LLM[Claude / LLM Runtime]
    end

    subgraph DataPlane["Data Plane (Go Runtime)"]
        FS[Filesystem Ops]
        Git[Git Ops]
        AST[AST & Dependency Analysis]
        Net[Network & Health Checks]
        Cmd[Command Execution Sandbox]
    end

    subgraph External["External Systems"]
        Code[Codebases]
        Infra[Docker / K8s / Cloud APIs]
        DB[(Databases)]
        Obs[Logs & Metrics]
    end

    U --> CLI
    CLI --> OODA

    OODA --> Planner
    Planner --> Executor
    Executor --> Reviewer
    Reviewer --> OODA

    Planner --> MCP
    Executor --> MCP
    MCP --> LLM
    LLM --> MCP

    OODA --> State
    State --> OODA

    MCP -->|Validated JSON| DataPlane

    FS --> Code
    Git --> Code
    AST --> Code
    Net --> Infra
    Cmd --> Infra
    Cmd --> DB
    Net --> Obs

Control Plane vs Data Plane

| Plane | Responsibility | Characteristics | |------|---------------|----------------| | Control Plane | Reasoning, planning, orchestration | Stateful, adaptive, model-driven | | Data Plane | Execution, IO, system interaction | Deterministic, high-performance |

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The Cognitive Core (Control Plane)

Runtime: Node.js / TypeScript Framework: LangGraph.js

The Cognitive Core is responsible for thinking, not doing.

Responsibilities

• Stateful, multi-turn agent reasoning
• Recursive planning and replanning via OODA loops
• Tool discovery and negotiation via Model Context Protocol (MCP)
• Enforcing structured outputs and execution constraints

Interface

SwarmLord exposes a reactive Terminal User Interface (TUI) built using Ink (React for CLI):

• Live agent reasoning and decisions
• Code diffs and execution previews
• Dependency graphs and execution timelines

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The Kinetic Layer (Data Plane)

Runtime: Go (Golang)

The Kinetic Layer is responsible for doing, not thinking.

Responsibilities

• Latency-critical and IO-heavy operations
• Deterministic system execution
• High-concurrency filesystem and network access

Capabilities

• AST Parsing — fast dependency analysis and code graph construction
• Filesystem Operations — high-concurrency search, refactors, and bulk edits
• Git Operations — diff generation, blame analysis, patch creation
• Network Probing — direct socket-based health checks

This layer is intentionally model-agnostic and fully auditable.

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MCP Tool Invocation Flow

flowchart LR
    Agent[Agent Reasoning]
    Schema[JSON Schema Validation]
    MCP[MCP Tool Registry]
    Tool[Go Tool Binary]
    System[Host System]

    Agent -->|Intent| Schema
    Schema -->|Validated| MCP
    MCP --> Tool
    Tool --> System
    System --> Tool
    Tool --> MCP
    MCP --> Agent

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Core Capabilities

Autonomous DevOps & SRE

• Self-Healing Infrastructure

Consume metrics (e.g., Prometheus), detect anomalies such as OOM kills or CPU starvation, and propose corrective actions like autoscaling or resource reallocation.

• Incident Remediation

Automated log analysis and root-cause triangulation using RAG (Retrieval Augmented Generation) against internal runbooks and historical incidents.

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Distributed Systems Engineering

• Legacy Modernization

Swarm-based decomposition of monolithic services into domain-aligned microservices, with autogenerated Go/gRPC interfaces.

• Architecture Verification

Validates system design against distributed systems best practices:

• Idempotent event handlers
• Safe retry semantics
• Correct transactional boundaries

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Agent Execution Modes (Swarm / Pipeline)

sequenceDiagram
    participant User
    participant Planner
    participant AgentA
    participant AgentB
    participant AgentC
    participant Reviewer

    User->>Planner: High-level objective
    Planner->>AgentA: Subtask 1
    Planner->>AgentB: Subtask 2
    Planner->>AgentC: Subtask 3

    par Swarm Execution
        AgentA->>AgentA: Execute
        AgentB->>AgentB: Execute
        AgentC->>AgentC: Execute
    end

    AgentA-->>Reviewer: Result A
    AgentB-->>Reviewer: Result B
    AgentC-->>Reviewer: Result C

    Reviewer-->>Planner: Consolidated Output

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Safety & Governance

• Human-in-the-Loop Execution

All destructive actions (database migrations, Terraform applies, production deploys) require explicit human approval.

• Deterministic Outputs

All agent-to-system calls enforce strict JSON schemas (via Zod), eliminating hallucinated commands.

• Sandboxed Runtime

Execution occurs within constrained environments with explicit capability boundaries.

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Technical Stack

| Component | Technology | Rationale | |---------|-----------|-----------| | Orchestration | LangGraph.js | Stateful, cyclic agent graphs | | UI | Ink (React) | Rich, reactive CLI interface | | Execution Engine | Go | High-performance concurrency | | Model Layer | Claude 3.5 Sonnet | Advanced reasoning and code synthesis | | Protocol | MCP | Standardized tool discovery and invocation |

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Engineering Roadmap

Phase I — The Kernel

• [ ] Primary command loop
• [ ] Ink-based TUI rendering engine
• [ ] MCP negotiation layer

Phase II — The Toolchain

• [ ] Go-based filesystem and Git interface
• [ ] Deterministic execution engine
• [ ] Schema-validated tool APIs

Phase III — The Graph

• [ ] Planner–Executor–Reviewer agent topology
• [ ] Recursive OODA loop implementation
• [ ] Failure recovery and replanning

Phase IV — The Mesh

• [ ] Docker integration
• [ ] Kubernetes and AWS providers
• [ ] PostgreSQL, Redis, and Kafka connectors

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Design Goals

• Treat AI as infrastructure, not a chatbot
• Enforce determinism at execution boundaries
• Enable long-running autonomous workflows
• Make backend engineering AI-native by default

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License

© 2026 Kritagya Jha All rights reserved.

> sudo-summon-swarm is an experimental system intended for research and advanced backend engineering use cases.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Kritagya123611
• Source: Kritagya123611/Swarm-Lord
• License: MIT

Install and usage instructions live in the source repository linked above.