Reduce costly bad AI agent actions.
BIGHUB evaluates agent actions before they run, learns from real outcomes, and helps future actions get judged more accurately.
Agent actions are judged with experience, not just rules.
Each action is evaluated in context, linked to real outcomes after execution, and judged with experience from similar past cases.
Action evaluation
Every agent action is evaluated in context before it runs. BIGHUB combines rules, simulation, precedents, and learned signals to judge whether the action looks safe, fragile, costly, or risky.
Safety floor
Hard limits still protect critical actions in real time. If an agent crosses financial, operational, or behavioral boundaries, BIGHUB can block or escalate immediately.
BIGHUB tracks what actually happened, learns which decisions were good or bad in context, and uses that experience to influence future decisions.
.01
Connect your agent runtime
Use the Python SDK, adapters, or MCP server to route agent actions through BIGHUB.
.02
Set the safety floor
Define the hard limits and approval points that should always apply before an action runs.
.03
Link actions to outcomes
Real outcomes are linked back to each action so similar actions get judged with more experience over time.
Pricing.
Free beta for early agent teams.
Talk to the BIGHUB Team
Design partner requests, enterprise needs, pricing, or product feedback.
Frequently Asked Questions.
Simple answers to what most teams ask before joining BIGHUB.
How long does integration take?
Use the SDK, adapters, or MCP server to start routing agent actions through BIGHUB.
Does BIGHUB replace our agents?
No. Your agents keep running. BIGHUB evaluates their actions.
What happens if an action crosses a hard limit?
It can be blocked, escalated, or sent for approval.
Do you store our data?
BIGHUB stores decision-related signals based on your setup, not general business data by default.
Can we define our own limits?
Yes. You choose the hard limits and approval points that matter.
How does BIGHUB improve future decisions?
It learns from real outcomes and similar past cases.
Where does BIGHUB fit in the stack?
BIGHUB sits on the action path before execution, with outcomes linked back after.
How is BIGHUB different from AI guardrails?
Guardrails enforce rules. BIGHUB also learns from real outcomes.
