Make the trade-offs visible.
Give your AI agent a decision to frame, then progressively inspect the criteria, hard constraints, grounded brief, and sensitivity drivers before committing.
Explore Example Decisions
See how Decision Workspace and WebMCP handle trade-offs, hard constraints, and sensitivity in real scenarios.
Used car under VND 200M
A commuter in Hanoi wants an automatic sedan around VND 200M, prioritizing running cost and safety.
Stay at current company or join a startup?
Compare a stable enterprise role with a high-upside Series-A startup offer.
Rent or buy an apartment?
A household considers buying their first home with crucial mortgage terms still unknown.
From question to grounded commitment
Guided Interview
Answer one question at a time with Answer, Later, or Skip. Hard constraints and preferences are separated immediately.
Review Details
Inspect the criteria matrix with sticky columns, normalized driver contributions, and sensitivity what-if calculations.
Decision Brief
Receive a grounded synthesis citing rationale, verified facts, open unknowns, and what conditions would reverse the choice.
The agent can work in the room. You keep the last word.
WebMCP provides atomic, revision-aware mutation tools for agents to frame decisions directly inside the browser. Every assessment has provenance, every unknown has a next step, and the final commitment remains human.