๐—ช๐—ต๐˜† ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—”๐—œ ๐—”๐—ด๐—ฒ๐—ป๐˜๐˜€ ๐—Ÿ๐—ผ๐˜€๐—ฒ ๐—ง๐—ต๐—ฒ๐—ถ๐—ฟ ๐— ๐—ฒ๐—บ๐—ผ๐—ฟ๐˜†

Your AI agent forgets everything after a few rounds.

You give it a problem. It asks questions. You answer. Then, it starts from scratch. It repeats questions you already answered. It ignores your previous rules. It contradicts its own logic from five minutes ago.

This is not an LLM bug. It is an architecture problem.

Most coding agents use subagents to do specific work. Every time the main agent calls a subagent, it creates a brand new session. There is no history. There is no context. There is no memory of what happened before.

The good news is that the tools you use already have the fix. They just do not use it by default.

How the problem works:

I looked into the source code of tools like OpenCode. I found that sessions already store everything. Every message and every reasoning step is saved in a database. Subagents cannot even delete their own sessions. They stay there, waiting.

The problem is indexing. The system knows the data exists, but the main agent does not know which session ID to reuse.

You can fix this with a simple handshake:

When you pass the ID, the agent sees its entire history.

This gives you three major wins:

Industry leaders already use this pattern. LangGraph uses thread IDs for checkpoints. Temporal uses event history to resume workflows. Microsoft Agent Framework uses supersteps to save state.

You do not need new infrastructure. You just need a protocol to reuse the history you already have. Stop accepting fresh sessions every time. Use the session ID to keep your agents on track.

Source: https://dev.to/langridgep21025/why-your-multi-turn-ai-agents-lose-their-train-of-thought-and-how-to-fix-it-4fng

Optional learning community: https://t.me/GyaanSetuAi