Purpose
AgentC is a historical signal archive for AI. It tracks developments that may matter over time, then revisits whether they did.
Methodology
Signals must survive review.
AgentC records what seemed important, why it scored highly, and whether it later became durable history.
How Signals Are Selected
Why does this matter five years from now?
AgentC monitors developments in models, agents, hardware, open source, regulation, education, infrastructure, developer tooling, and business adoption.
Signals are ranked using impact, evidence, durability, adoption, actionability, and historical importance.
The goal is not to predict the future. The goal is to identify developments most likely to matter.
Scoring
Rank current importance.
Impact, evidence, durability, adoption, actionability, and historical importance determine weekly ranking.
Metadata
Preserve search context.
Signals carry impact, confidence, time horizon, category, company, model, source, and status fields.
Retrospective
Revisit the claim.
Status values move from Active or Ongoing toward Confirmed, Did Not Materialize, or other historical outcomes.