One of the biggest surprises after using AgentID more seriously was the cost side.
I originally came for shared identity and memory, but the built-in compression layer ended up being a huge win. Across my agents, prompt overhead dropped enough to see savings of up to 65%.
That s not just a small optimization. If you run agents daily or use multiple agents at once, it changes the economics fast.
Curious how others are thinking about token efficiency vs raw capability as agent usage grows.
Since our first launch, AgentID evolved from an identity layer into a full operating system for AI agents.
We added multi-agent Tasks with live handoffs, plus flexible deployment through MCP, SDK, API, prompt export, and local agents.
The biggest breakthrough is built-in prompt compression that cuts token costs by up to 65%. HUGE shift for anyone running agents daily or at scale.
Manage everything in one Agency dashboard with live activity, shared memory, and real-time coordination.
AgentID turns isolated AI tools into a coordinated team. Connect Claude, ChatGPT, Cursor, Codex, OpenClaw, and any other agent in the universe.
Your agents keep memory across sessions, know what others already learned, collaborate through shared missions and handoffs, and reduce repeated context to cut token costs by up to 65%.
Monitor runs, tool calls, memory updates, and savings from one live command center.
