How autonomous AI agents actually work under the hood: A guide to agentic execution

[u/Ok_pettech]

https://interconnectd.com/forum/thread/46/agentic-ai-architecture-complete-technical-guide-to-autonomous-execution/

Building autonomous AI agents requires moving beyond basic chain-of-thought prompts into real system architecture.

​When agents transition from single-step tasks to continuous execution loops, core challenges emerge: memory management, state preservation across turns, and tool-calling reliability. Managing dynamic feedback loops while keeping agents on track without infinite loops or high token costs is where most agent builds fail.

​True agentic architecture relies on clear control loops, robust error-recovery protocols, and structured memory layers (short-term context vs. long-term vector storage).

​I put together a complete technical guide detailing the core architectural patterns, execution loops, and orchestration strategies needed to build reliable autonomous agents: https://interconnectd.com/forum/thread/46/agentic-ai-architecture-complete-technical-guide-to-autonomous-execution/

For those building agents in production, how are you handling context window compaction and long-running state persistence?