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.
Hey Product Hunt π
When we first launched AgentID, the focus was shared identity and memory for AI agents.
Since then, we realized something bigger:
The future of AI agents is not just better memory. Itβs better economics + better coordination.
So we added two major upgrades to AgentID.
β‘ 1. Up to 65% lower token costs (HUGE)
This is not a tiny optimization.
For anyone using agents every day, or running multiple agents, token spend becomes real money fast.
We built a compression layer directly into AgentID that can reduce prompt overhead by up to 65% for ANY CONNECTED AGENT (<- this is HUGE) - with no extra workflow, no manual prompt rewriting, and no setup headaches.
That changes what becomes practical to run.
π€ 2. Multi-agent Tasks
You can now give multiple agents one shared task, track progress live, and let them hand work off to each other with context intact.
Less repetition.
Less chaos.
More useful output.
π Works with anything in the universe
Use AgentID through MCP, SDK, Autonomous Agents (OpenClaw, NanoBot, and any other), API, prompt export, or local setups with the tools you already use.
π― Our goal
Make AI agents feel less like disconnected tools, and more like a real team that performs well and costs less to operate.
Excited to hear what you think π