Temporal Chains: Reconstructing Causal Steps in Agent Decisions

If an AI agent deploys a patch that breaks a build, it is not enough to look up the last file edit. The agent needs to trace the causal timeline of inputs and decisions that led to the deployment.

Spector tracks these sequences using TemporalChain links.

Causal Timeline Replays

Every action, command, and feedback loop is appended as a node in the temporal chain. When an error is encountered, Spector traces backwards:

[User Request] -> [Code Edit] -> [Build Command] -> [Test Run] -> [Failure Event]

Replaying Decisions for Context

By presenting this chronological chain to the LLM, the agent can isolate the exact step that introduced the failure, offering a clear causal explanation instead of guessing based on similarity.