Embedded Risk Analytics·Enabling a more coherent future
Coherence middleware for long-horizon AI agents.
ERA Fathom measures where an agent loses coherence, attributes the cause, and repairs it in line.
The current cost of coherence
On both AWS Bedrock AgentCore and Azure AI Foundry, managed memory keeps a committed state for long-horizon agents. Fathom keeps it as a deterministic, auditable, cost-bounded record, and recovers one fact at a fraction of the tokens the managed memory path spends.
Tokens spent to re-ground a single prior decision. Lower is better.
Bars are drawn to linear scale; the Fathom record is the teal sliver at 1×. Measured identically on both clouds. The native path recovers the fact probabilistically and leaves no audit trail.
A new information theory
The market's answer to agent memory is retrieval and summarization. These are probabilistic patches: they grow more expensive as a task lengthens, and the next action still has no guarantee of seeing the state it depends on.
Fathom starts from the accounting problem underneath. To act coherently, an agent must hold a faithful record of what it has already done, and it can hold that record only while the demand on its record layer stays within the layer's capacity. That is the necessary condition for coherence, and it takes the form of a feasibility inequality.
The inequality is a stability condition carried over from control theory. Its terms separate ordinary task difficulty from the reflexive burden an agent adds by operating on its own record, and that burden is what pulls an agent off its own state as horizons grow. Fathom holds committed state to the inequality: a deterministic, auditable record that keeps the demand bounded and coherence intact.
Learn about Fathom