I'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts.

The architecture aims to solve critical gaps in deterministic orchestration identified by *Prof. Claudionor Coelho Jr. (Stanford alum, ML/DL Faculty at Santa Clara Univ., and Senior Fellow for AI at Majestic Labs)* during our work on the Kiroku project.

*Key Technical Features:*

* *Neurosymbolic Native:* We integrated Prolog to logically validate LLM outputs. This combines neural flexibility with symbolic reasoning to help mitigate hallucinations.

* *YAML + Overlays:* Agents are defined in YAML with overlay support (similar to the Kustomize pattern in Kubernetes), making configs testable and reproducible across environments (Dev/Prod) without code duplication.

* *Hybrid Scripting:*

* *Lua:* Embedded in all binaries (Python, Rust, Wasm) for secure, lightweight logic at the Edge.

* *Python:* Full integration for data science workloads.

* *Batteries Included:* We implemented 110+ tools based on Sarwar Alam’s Agentic Design Patterns. https://github.com/sarwarbeing-ai/Agentic_Design_Patterns

* *Polyglot:* Core written in Rust/Python with Wasm support (runs in browser, Docker, or embedded).

* *Observability:* Native hooks for Comet (Opik) to track execution/cost.

The goal is to provide a solid engineering foundation for agents. I’d love to hear your feedback on the Prolog integration and the YAML-based architecture.

Repo: https://github.com/fabceolin/the_edge_agent

Demo (Wasm): https://fabceolin.github.io/the_edge_agent/wasm-demo