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The knowledge graph is where everything Lace captures accumulates: screens, actions, and the comments your team pins along the way. Lace AI reasons against this record, so its answers draw on what your team has already seen and settled instead of starting from zero.

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What gets captured

A snapshot of what’s on screen at a point in time.

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How the graph builds over time

Pattern recognition runs against your interaction history:
  1. Transitions accumulate into a map of how you and your team move through your work.
  2. Patterns emerge: loops, dead ends, hot spots, escape points.
  3. Evidence is assembled with metrics, states, and UI content.
  4. Context compounds: Lace AI draws on the full graph when reasoning about your work in chat.

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From capture to agent execution

  1. Detect. The graph identifies a pattern worth investigating.
  2. Reason. You ask Lace AI about it, or Lace AI raises it during a chat grounded in a live capture. For earlier work in the same or past sessions, agents load the session flow through MCP (search_sessions → get_session_flow).
  3. Decide. You pin a comment in Reviews and resolve it into a decision, tied to the spot where it happened.
  4. Execute. Your agent queries the decision through MCP and carries it out with the full context behind it.