The Auton Agentic AI Framework

Key points

There is a fundamental mismatch between the probabilistic outputs of large language models, or LLMs, and the deterministic, schema-compliant input requirements of backend infrastructure, a problem the paper calls the integration paradox.
The widespread fragmentation of the agent development ecosystem is characterized by the absence of unified standards, which leads to vendor lock-in, poor auditability, and limited portability across languages.
Current LLM-based agents are often stateless, lack persistent memory, and provide limited safety guarantees, which makes them difficult to deploy in real mission-critical environments.
It strictly separates an agent specification layer, a declarative, language-agnostic cognitive blueprint called AgenticFormat Standard, from an execution layer, a platform-specific runtime engine.
To ensure deliberate behavior, it presents a formal agent execution model based on an extended partially observable Markov decision process, or POMDP, with a latent reasoning space and factored policy structure.
It presents a hierarchical memory structure for cognitive persistence and a constraint-manifold formalism that enforces safety by design through policy projection during action generation.

External research summaries. These are not HDATF publications or measured product results.