Agentic AI Engineer

Agentic AI is harder than it looks in demos. A chatbot that calls one tool is easy. A system that autonomously plans multi-step tasks, recovers from failures, manages tool outputs that are too large for the context window, and knows when to stop and ask for clarification rather than proceeding confidently in the wrong direction - that is a different engineering problem.

The core challenges are tool reliability, context management, and controllability. Tools fail. APIs return unexpected formats. A task that should take three steps sometimes requires twelve because an intermediate result was not what the model expected. Good agentic systems handle this gracefully - retrying with corrected inputs, falling back to alternative strategies, and surfacing failures to the user when they cannot be recovered automatically.

Controllability is equally important. An agent that autonomously sends emails, edits files, and runs code needs hard boundaries on what it can and cannot do. I designed LUNA's skills system with this in mind: each skill declares its capabilities and side effects explicitly, and destructive operations require confirmation before execution. Autonomy with control, not autonomy instead of control.

LUNA's plugin system is designed around a constraint: the core engine should not need to know anything about the skills that extend it. Each skill is a folder containing a skill.json that declares its name, description, and parameters, and a SKILL.md that tells the language model how to use it. The engine reads the skill manifest at startup, exposes the skill to the LLM as a tool, and routes calls at runtime. No code changes. No restarts.

This architecture makes LUNA genuinely extensible without becoming complex. Adding a new agent capability - say, a skill that queries a specific internal API - does not require understanding how the core routing works, how the LLM is configured, or how streaming responses are managed. You write the skill, declare its interface, and drop it in the directory.

The same architecture makes skills composable. The Research Agent can call the Coding Agent to execute Python that processes the data it found. The Desktop Agent can use the Workspace Suite to email results. Multi-step agentic workflows emerge from composition of simple skills rather than from complex orchestration logic in the core.