New AI Agent Architecture to fix LLM deviations and token costs

If you’ve use AI agents, you know the struggle: you drop massive, complex workflow instructions into a system prompt, and the LLM eventually gets confused, deviates from the plan, or racks up a massive token bill. We just published our new open-source repo,**BotCircuits Agent**, that solves this by fundamentally changing how the agent loop works. We’ve implemented a **Workflow-native Agent Loop** Architecture that splits the workload: * **Deterministic State-Machine Runner:** Handles all workflow navigation and conditional routing with **zero LLM usage**. It runs the logic exactly as designed. * **Targeted LLM Execution:** The workflow runner pauses at specific steps and hands control over to the LLM agent *strictly* to execute a single action using connected Tools, Skills, or MCPs. * **The Loop:** Once the tool action is complete, control is instantly handed back to the deterministic runner to navigate to the next step. **Why this matters:** The LLM is only ever aware of the *current* executing action. Because it no longer has to digest and interpret lengthy, multi-step instruction prompts, you get two massive benefits: 1. **Significant reduction in deviations ( predictability ):** The agent can't go off-track because the state machine controls the rails. 2. **Significant reduction in token usage ( less costs ):** Smaller context windows per step equal cheaper runs. Would love to hear your feedback or answer any questions! Link to the GitHub repo is in the comments.