Agents that update their model of the environment during inference
A post published on June 30, 2026, about ARC-AGI-3 says that learning during inference leads agents to build models of rules and mechanics, updating them with new evidence.
Source: Continual Harness para agentes no ARC-AGI-3 (x.com). Text prepared with AI from this source.
What happened and what to do
A post published on June 30, 2026, about ARC-AGI-3 describes “Continual Harness: An Efficient Self-Improving Agent on ARC-AGI-3” as an agent whose learning during inference requires an internal model of the environment’s rules and mechanics. That model is updated as new evidence becomes available.
A company could apply this approach to agents handling variable processes: log evidence and state changes, maintain an explicit model of relevant rules, and test whether updates improve decisions without causing regressions. The implementation should include evaluation criteria and controls on automatic changes.
How the consultancy can help
Wendelmaques can diagnose the opportunity and risks, scope an agent with state memory and an updateable model, and implement its evaluation, monitoring, and operation on the company’s infrastructure.
Next step
Send a short description of the process or agent you want to assess to receive a scoped proposal.
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