How to Run 15 AI Agents at Once — A Practical Guide to Parallel AI Orchestration

AI sessions are volatile, so it's easy to solve the same problem again and repeat the same mistakes. That's why accumulating the knowledge gained during work in an external memory system lets you pull up decision rationale and troubleshooting context from 3 months ago.

The knowledge accumulation flow revolves around Obsidian. Findings discovered during work are automatically logged to Obsidian, local embeddings are created with the QMD plugin, and semantic search is performed with the recall skill. Attach Obsidian MCP on top of this so the AI can read and write directly, and sessions stop being one-off and become an accumulating asset.

The setup consists of the following:

• Obsidian: a local markdown knowledge store that saves session summaries, architecture decisions, and troubleshooting notes
• QMD plugin: an indexing layer that generates embeddings locally without an external server
• recall skill: a search layer that asks "was there a similar task before?" and retrieves relevant documents
• Obsidian MCP: the connector that lets the AI directly read and write to Obsidian

The execution process begins with deep-interview planning. Before implementation, you need to pin down requirement scope, existing codebase patterns, external constraints, success criteria, and parallelizable units as clearly as possible to reduce rework. Quality improves when you give the AI "under these conditions, in this order, to this standard" rather than just "build this."

Next come two parallel strategies. Task DAG breaks implementation down into a topologically sortable task graph to parallelize it, while Branch Hunt traverses code via DFS and, whenever an issue is found, spins up an independent branch session for parallel debugging. Task DAG is mainly used for new development and refactoring, while Branch Hunt is mainly used for debugging and code review.

As a real-world example, a task like an instant payment-blocking feature that spans multiple servers and repos was broken down into a DAG and processed in parallel, finishing in a single day what would have taken several days. By separating pg-front and member-api, the work moved step by step through exploration, core service implementation, API blocking, refactoring, review, testing, and integration verification, reducing bottlenecks along the way.

The final pillar is the Ralph Loop. Once implementation begins, automated verification (Build · Type Check · Test) is run; if it passes, the loop moves to the next task, and if it fails, the loop fixes the issue and re-verifies. Through unattended automatic execution with built-in verification, this structure pushes work forward until completion criteria are met without requiring continuous human intervention.

Overall, the core point is clear: rather than simply using AI well, it's more important to divide work well, make it remember well, and verify well. Explore deeply, execute in parallel, externalize memory, and tie verification into loops — that's what it takes to put AI agents to real use in actual work.