If your autonomous AI agent takes 10 steps to solve a problem, and each turn takes 5 seconds, your user is waiting nearly a minute.

This is the Cognitive Latency Cascade, and it kills the user experience of local AI.

To make local-first agents practical, we re-engineered the inference path in our Silex Engine. Here is how we got agent turn latency down to sub-second speeds on consumer hardware (like an RTX 4070):

Prompt Caching (Keeping Context Hot) Normally, changing a single character at the end of a prompt forces the model to re-prefill the entire context. In agent loops, context is constantly changing as tool results get appended. We partitioned the context: keeping tool definitions and system prompts in a cached prefix, and only processing the new tool results in the suffix. → Result: Prefill time dropped from 4.2s to 0.3s.

GBNF Grammars (Zero Parser Failures) Most developers let the LLM generate raw JSON, parse it, and retry if there's a syntax error. This double-inference penalty wastes precious seconds. We use GBNF grammars to constrain token selection at the sampler level. The model is physically incapable of outputting malformed JSON. → Result: Average step latency reduced by 18%.

Active Context Pruning A 200-line compiler error log quickly bloats the context window. We store full execution logs in a local SQLite database, and represent them in the active context as a single summary node. The agent only retrieves the full log if it explicitly needs to debug that node.

By combining prompt caching, grammar constraints, and context pruning, we made our agents (Kronos and Vyn) feel responsive and alive.

I wrote a detailed technical breakdown of these optimizations: https://openyf.dev/blogs/agents-on-the-edge

#AIAgents #LocalAI #MachineLearning #Python