Agentic AI with Antigravity & MCP Runbook | Joey Jones

Key Architectural Outcomes & Engineering Deliverables

Core verification criteria and mission milestones established by this document

Architects autonomous multi-agent swarms with clean role delegation and isolated workspace sandboxing.
Standardizes tool access across external databases and APIs using standardized Model Context Protocol (MCP) servers.
Implements progressive context disclosure to prevent token budget exhaustion during recursive reasoning.
Embeds automated pre-commit and post-execution security guardrails to eliminate hallucination risks.

1. Overview & Operational Principles

Autonomous AI agents transition LLMs from conversational chatbots into autonomous pair engineers. The Google Antigravity SDK combined with the Model Context Protocol (MCP) provides a production-grade framework for tool invocation, environment introspection, and recursive task completion.

This runbook documents the engineering standards for building high-reliability agents that write code, debug infrastructure, and deploy systems with deterministic accuracy.

2. Model Context Protocol (MCP) Server Setup

The Model Context Protocol standardizes how language models connect to external tools, databases, and environments without custom glue code.

{
  "mcpServers": {
    "git-operations": {
      "command": "node",
      "args": ["/opt/mcp/servers/git-mcp/dist/index.js"],
      "env": {
        "GIT_AUTHOR_NAME": "Antigravity Agent",
        "GIT_AUTHOR_EMAIL": "agent@joeyajones.com"
      }
    },
    "edge-kv": {
      "command": "wrangler",
      "args": ["kv:bulk", "get", "--binding=CATALOG_KV"]
    }
  }
}

3. Subagent Delegation Topology

Complex software workflows must never be handled by a single monolithic agent turn. Use specialized subagents with strictly scoped capabilities:

                     +---------------------------+
                     |    PRIMARY ORCHESTRATOR   |
                     |  (Antigravity Planner)    |
                     +-------------+-------------+
                                   |
             +---------------------+---------------------+
             |                                           |
             v                                           v
+-------------------------+                 +-------------------------+
|    RESEARCH SUBAGENT    |                 |   EXECUTION SUBAGENT    |
| - Read-only filesystem  |                 | - Workspace branch root |
| - Web/Docs search tools |                 | - CLI compile & test    |
| - Semantic code grep    |                 | - Git stage & commit    |
+-------------------------+                 +-------------------------+

4. Progressive Disclosure & Context Budgeting

To ensure the agent maintains high reasoning fidelity over 50+ turn workflows:

Lazy Tool Mounting: Never preload 50 tool schemas into the initial turn. Expose root router tools and dynamically mount schemas via call_mcp_tool.
Artifact Directory Persistence: Save intermediate analysis, state machines, and execution diffs into designated <appDataDir>/brain/<conversation-id>/ directories rather than repeating large logs in model context.
Execution Verification: Every tool invocation that mutates state must immediately follow with a verification command (e.g. npm run build or git status).

5. Production Agent Checklist

Model configured with strict temperature control ($T=0.1$ for code generation, $T=0.4$ for exploratory planning).
Destructive commands (rm -rf, DROP TABLE, git push --force) intercepted by accidental data loss prevention hooks.
Subagent workspaces branched or cloned to prevent dirty working trees in production repositories.
Comprehensive telemetry and token consumption metrics logged per agent session.

Indexed Taxonomy & Vector Tags

#Agentic AI#Google Antigravity SDK#Model Context Protocol#MCP#Autonomous Agents#Subagent Orchestration#Gemini API#JSON-RPC Tooling#LLM Engineering#Deterministic Guardrails

Autonomous Agentic Engineering with Google Antigravity & Model Context Protocol (MCP)