AgentID

AgentID solves a critical problem in multi-agent AI workflows: maintaining consistent identity, memory, and context as tasks move between different AI models and tools. Instead of starting from scratch with each new agent or platform, AgentID creates a unified layer that preserves conversation history, task state, and agent identity across Claude, ChatGPT, Cursor, Codex, and dozens of other AI systems. The platform supports multiple connection methods—MCP (Model Context Protocol), SDK, API, and direct prompt integration—making it flexible enough to fit into existing AI stacks without major refactoring. Users can monitor all connected agents in real time, gaining visibility into multi-agent operations and making it easier to debug, audit, and optimize workflows. By consolidating memory and context management, AgentID dramatically reduces redundant token usage. The claimed 65% reduction in token overhead translates directly to lower API costs and faster response times, making complex agent-to-agent collaboration more economical. This efficiency gain is especially valuable for organizations running large-scale AI agent deployments or complex multi-step automation workflows.