GACKS P.A.
GACKS P.A. V2 is an architectural evolution from a local voice assistant into a production-grade, persistent, multimodal personal AI operator. It understands context, creates structured execution plans, invokes sandboxed tools, verifies outcomes against real systems, and seamless integrates with screens and cameras.
01 • Problem Statement
Conventional chat assistants lack persistent mission state, multimodal screen/camera awareness, deterministic tool execution boundaries, and closed-loop outcome verification.
02 • Deployment & Runtime
Vercel Frontend Deployment with containerized Docker execution environment support.
System Architecture Pipeline
Voice, screen share, and camera frame capture interface
Working state, episodic mission history, and semantic pruning
Dynamic task decomposition and routing between Gemini and Claude
Schema-validated tool invocation and closed-loop outcome verification
Architecture & Core Features
- •Context Engine
- •Orchestrator
- •Planner
- •Model Router
- •Tool Execution Registry
- •Verifier
- •Persistent Missions
- •Multi-Tier Memory
- •Closed-Loop Verification
- •Voice & Vision Integration
Engineering Challenges & Solutions
- •Preventing hallucinated tool invocations during multi-step autonomous missions
- •Managing short-term conversational context alongside long-term episodic memory without token bloat
- •Synchronizing low-latency WebRTC audio/video streams with asynchronous planner states
- Separated the cognitive loop into distinct Context Engine, Planner, Model Router, Tool Registry, and Verifier modules
- Implemented strict schema validation on all tool calls with sandboxed execution boundaries
- Built a multi-tier memory hierarchy with deterministic context pruning
Requirements & Technical Governance
- • Support multimodal inputs including voice, text, screen capture, and camera streams
- • Route tasks dynamically across frontier LLMs (Gemini, Claude)
- • Verify tool execution outcomes before reporting mission completion
- • Explicit tool permission registry preventing unauthorized system commands
- • Strict environment-variable isolation for LLM provider credentials
- • Streaming token responses paired with incremental state updates
- • Context window budgeting to maintain predictable LLM response latency
- • Real-time agent step logs tracking Planner -> Tool -> Verifier state transitions
Verified Outcomes & Technology Stack
- →Production-deployed interactive operator interface at gacks-ai.vercel.app
- →Demonstrates complete agentic orchestration, tool registry governance, and multimodal state management
- →A dedicated Verifier stage after tool execution drastically reduces silent agent failures
- →Explicit model routing allows pairing fast models for UI intent with deep reasoning models for planning