About Dialetica AI
The orchestration layer for multi-agent intelligence
Today, every major AI model — OpenAI, Anthropic, Google — is a single agent. But humans don't reason alone, neither should AI. Progress happens through debate, dialogue, and synthesis. That's the gap we're solving.
Dialectical AI builds the orchestration layer for multi-agent intelligence — the middleware between foundation models and rich, collaborative reasoning.
While OpenAI, Anthropic, and Google build the agents, we build the brain that unites them.
The Problem
Large language models today are solitary minds — they simulate intelligence but lack dialogue, self-critique, and consensus formation.
Even the best chatbots (ChatGPT, Claude, Gemini) are bound to one-to-one interactions, missing the cognitive power of multi-perspective reasoning.
Orchestration frameworks are ad-hoc, complex, and inaccessible to developers.
They are usually implemented as graphs or state machines, requiring manual configuration of roles, message flows, and decision logic. This makes them hard to scale, and nearly impossible to adapt to dynamic, real-world dialogues.
As a result, even research-grade multi-agent systems remain confined to academic settings or high-budget labs.
Our Solution
Dialetica AI introduces an orchestration engine that manages multi-agent reasoning.
It coordinates agents, detects convergence, and enables true multi-agentic conversation spaces.
Smart Coordination
Automatically routes conversations to the right agents
Convergence Detection
Identifies when agents reach consensus
Dynamic Dialogue
Adapts to real-world conversations
Core Concepts
Understanding the fundamental building blocks of Dialetica AI
An Agent is a specialized AI entity with unique characteristics, instructions, and capabilities. Each agent is powered by a foundation model (like GPT, Claude, or Gemini) but configured with specific:
- •Instructions: System prompts that define behavior and expertise
- •Model Configuration: Temperature, max tokens, and provider settings
- •Tools: Access to MCP (Model Context Protocol) tools for extended capabilities
- •Knowledge: Agent-specific information that shapes responses
A Context is a conversation environment where one or more agents interact. Think of it as a "room" or "workspace" where multi-agent dialogue takes place. Contexts enable:
- •Multi-Agent Collaboration: Multiple agents working together on complex tasks
- •Conversation History: Persistent message history shared across all participants
- •Dynamic Routing: Automatic selection of the best agent to respond
- •Shared Knowledge: Context-wide information accessible to all agents
- •Smart Context Window: Automatic message compression to manage long conversations efficiently
Context Window Management:
Contexts automatically manage conversation history through intelligent compression when token limits are reached. You can configure the window size (default: 16,000 tokens) or disable storage entirely (set to 0) for stateless interactions.
A Message represents a single communication within a Context. Messages can be from users or agents and contain:
- •Role: Either "user" or "assistant"
- •Sender Name: Identifies who sent the message
- •Content: The actual message text
Knowledge represents information that agents can access and reference. Knowledge entries are automatically embedded for semantic search and can be:
- •User-Level: Available across all contexts for a user
- •Context-Level: Available to all agents in a specific context
- •Agent-Level: Exclusive to a specific agent
- •Searchable: Queried using semantic similarity for relevant retrieval
MCP (Model Context Protocol) Tools extend agent capabilities by connecting them to external services and APIs. Tool configurations allow agents to:
- •Connect to External Services: Notion, GitHub, databases, and more
- •Execute Actions: Create tasks, fetch data, update records
- •Secure Authentication: Manage credentials separately from agent configs
- •Reusable Configurations: Share tool configs across multiple agents
- •Recommended Integrations: You can find a list of useful servers at mcpservers.org. For straightforward and unified integration with Google, Notion, Zoom and others, we recommend waystation.ai.
Cron Jobs enable scheduled, automated execution of prompts within contexts. Use crons to:
- •Schedule Regular Tasks: Daily reports, weekly summaries, periodic checks
- •One-Time Execution: Schedule a task for a specific date/time
- •Standard Cron Expressions: Familiar syntax like "0 9 * * *" for daily at 9 AM
- •Context Integration: Crons execute within contexts, triggering agent responses