Grounded Reasoning Systems for Cloud Architecture - Iman Makaremi

As LLMs move into enterprise workflows, developers face a new kind of architecture challenge: how do you build reliable, interpretable systems powered by agents and reasoning? This talk unpacks how we designed and implemented an AI orchestration framework for enterprise architecture — combining LangGraph for multi-agent workflows, Flyte for distributed execution, and AWS Bedrock for LLM inference using Claude 3. The product: an AI copilot for enterprise architects, deeply rooted in your tech stack context. At the core of this system is a domain-specific **knowledge graph** that acts as long-term memory for the agents. It enables persistent, structured representations of architectural state, system dependencies, and business context — giving the agents the grounding they need to generate accurate recommendations, translate natural language into SQL or code, and maintain continuity across workflows. We’ll also cover how we’ve integrated observability practices — including planned OpenTelemetry instrumentation — to trace and debug autonomous AI systems in production. If you’re a developer or AI engineer thinking beyond the chatbot and looking to embed reasoning into complex system design and data tasks, this talk offers an end-to-end blueprint — from orchestration and grounding to production monitoring.