Council of AIs
Multi-Agent AI Framework
The Problem
AI agent development is fragmented. No unified approach spans from simple workflow automations to fully autonomous systems. Teams face a choice: use limited no-code tools or build everything from scratch.
The Solution
A framework spanning the full complexity spectrum. Entry-level automations, mid-tier orchestration, and autonomous agents with persistent memory — all within a unified architecture.
The Complexity Spectrum
Entry
Workflow Automation
n8n workflows, simple Python servers, MCPs. Task-specific automations that handle repetitive work.
Mid
Orchestrated Agents
Python-built task-oriented bots and agents that utilize APIs, Slack integrations, and multi-step workflows with MCP tool access.
Advanced
Autonomous Systems
ALAN: a modular, memory-driven autonomous system with persistent intelligence, MCP tool integration, reinforcement learning, and multi-agent coordination across complex workflows.
Flagship: ALAN
Autonomous Logic & Analysis Network. A modular, memory-driven, self-evolving system with 7 specialized components: Eyes (perception), Brain (GPT-powered reasoning), Memory (3-layer architecture), Executor (action), Instinct Engine (pattern matching), Coach (nightly learning), and MetaCoach (system diagnostics).
Full specification complete. Philosophy: GPT as a tool, not the brain.
Key Capabilities
Workflow Orchestration
Coordinate multi-step agent processes across platforms and APIs.
Autonomous Execution
Agents that act with context, learn from outcomes, and improve over time.
Market Analysis
Applied AI agents for financial data, prediction markets, and trading strategies.
Multi-Agent Coordination
Systems that don't just respond. They collaborate, delegate, and adapt.
Current Status
In active development. Working agent prototypes across multiple complexity levels. Task-oriented Python agents running with API and MCP integrations. ALAN specification complete.