Why agents need more than a fresh web search
A web search can help an agent find what a competitor says today. It does not reliably answer what changed, when the transition happened, or which captured evidence supported the conclusion. Those questions require durable state: an approved target, a baseline, a later observation, and a record that can be retrieved again.
Market Changelog is being built around that memory layer. It monitors operator-approved public pages, records material transitions as stable change events, and lets an agent retrieve the source context before using the result.
chg_ identifier.A useful agent loop
The basic workflow is intentionally small: add an approved target, queue a scan, check its status, search the workspace's change history, and retrieve a specific event. The search result is a lead for the agent; the evidence record is what supports the next conclusion.
This is especially useful for product research, pricing analysis, sales enablement, and recurring market reviews where a team needs to distinguish a known transition from page noise.
Website change monitoring for AI agents should produce a history
Repeated retrieval alone produces disconnected snapshots. A monitoring layer becomes agent-ready when it preserves the relationship between a baseline, a later observation, and the event created from that transition. That history lets an agent answer a multi-day question without treating today's page as the whole record.
What “reliable memory” means here
Reliability does not mean that every website can be parsed perfectly or that every detected change is strategically important. It means the monitoring work is durable, failures are visible, events have stable identities, and stored source material is clearly treated as untrusted external content.
Current stage: Market Changelog is in a small private design-partner pilot. Onboarding is manual, target lists are reviewed, and there is no public account creation, billing flow, OAuth connection, or service-level agreement today.
Where MCP fits
Model Context Protocol gives compatible agents a typed tool interface for this memory. The interface keeps target management, scan status, change search, and evidence retrieval explicit. Read the MCP competitive-intelligence guide for the complete flow, or see how the same model applies to competitor pricing monitoring.
Evaluate the evidence with us.
The private pilot is for people and teams with a recurring competitive question and up to five approved public pricing, documentation, changelog, release-note, or blog pages.
Request pilot access