Single-model agent pipelines are fragile. When your LLM provider encounters latency spikes or schema...
Single-model agent pipelines are fragile. When your LLM provider encounters latency spikes or schema drift, your entire business workflow stalls.
Here is how to design an enterprise-grade agent with automated failover in Python.
The Problem with Naive Agent Loops
Most LangChain or basic Python agent implementations look like this:
User input -> LLM -> Tool Call -> Response.
Error compounding, stale state, and context rot break multi-agent LLM pipelines. Learn the failure modes and design patterns that…
Run AI model failover drills with schema-safe retries, fallback contracts, circuit breakers, golden tasks, and recovery logs…
The naive assumption when building with LLMs is that a smarter single agent solves more problems....
When you chain language model calls into a pipeline, things break in unexpected ways. A step times...
Run a small Python lab that discovers agents, delegates a task over A2A, and returns a reviewable research-to-write result.
Explore critical architectural flaws in AI agents beyond LLMs, focusing on security (MCP), planning, and integrating agents into…
