I built an LMM (logic mental model) for building AI apps.

I naturally post about models (have a bunch on HF) over tools in this sub, but I also use tools and LLMs to develop agentic systems, and find that there is this mad rush to use the latest agentic framework as if that’s going to magically accelerate development. I like abstractions but I think mental models and principles of agentic development get rarely talked about which I believe can truly unlock development velocity.

Here is a simplified mental model that is resonating with some of my users and customers – separate out the high-level logic of agents from lower-level logic. This way AI engineers and AI platform teams can move in tandem without stepping over each others toes. What is the high-level logic?

High-Level (agent and task specific)

• ⚒️ Tools and Environment Things that make agents access the environment to do real-world tasks like booking a table via OpenTable, add a meeting on the calendar, etc. 2.
• 👩 Role and Instructions The persona of the agent and the set of instructions that guide its work and when it knows that its done

Low-level (common in most agentic system)

• 🚦 Routing Routing and hand-off scenarios, where agents might need to coordinate
• ⛨ Guardrails: Centrally prevent harmful outcomes and ensure safe user interactions
• 🔗 Access to LLMs: Centralize access to LLMs with smart retries for continuous availability
• 🕵 Observability: W3C compatible request tracing and LLM metrics that instantly plugin with popular tools

As an infrastructure tools and services developer in AI, I am biased – but would be really curios to get your thoughts on this topic.