Lattice vs Tanda

Lattice runs AI Agent as its own changelog category alongside Performance, Compensation, and Analytics, and the June work built it into something worth opening: the Agent attends 1:1s and captures summaries, action items, and coaching insights, answers data questions with charts rather than prose, and takes voice input. July then pointed the same capability outward. An MCP server exposes Lattice to Claude, OpenAI, and Slack, and AI review drafts are assembled from a person's own 1:1s, feedback, goals, and updates with cited sources. Underneath both, the performance and compensation machinery keeps absorbing manual setup — calibration groups generated from the org chart, approved promotions carried from a review cycle into a comp cycle.

The interesting move is that Lattice stopped assuming it wins by being the app you open. June made the Agent a destination; July made Lattice reachable from Claude, ChatGPT, and Slack instead, which is a bet that being the HR data layer beats owning the chat surface. Review drafts show what that data layer is being accumulated for: the meeting capture, feedback, and goals the Agent has been collecting are now the raw material for the review itself, with citations as the trust mechanism. Compensation is on a slower and more procedural track, closing loops — sending recommendations back for revision, exporting with HRIS IDs attached — rather than adding AI.

Expect the review-drafting work and the MCP surface to converge, so a manager can draft an evidence-cited review from Claude or Slack without opening Lattice. Whether compensation gets the same treatment is genuinely unclear from these entries — that area is still being hardened procedurally, and approval chains carry consequences that make an AI draft a harder sell.