Don't Build Multi-Agents
9 days ago
- #LLM Frameworks
- #Context Engineering
- #AI Agents
- Frameworks for LLM Agents are currently disappointing, and principles for building reliable agents are needed.
- React's success is due to its philosophy of reactivity and modularity, a lesson for AI agent development.
- Current agent-building lacks standardization, with some libraries promoting flawed multi-agent architectures.
- Reliability in agents requires Context Engineering to prevent compounding errors and maintain coherent conversations.
- Context Engineering is about dynamically providing the right context, crucial for effective AI agents.
- Multi-agent architectures are fragile due to potential miscommunications and inconsistent subtask execution.
- Principle 1: Share full context and agent traces, not just individual messages, to maintain coherence.
- Principle 2: Actions carry implicit decisions; conflicting decisions lead to bad results.
- Single-threaded linear agents are simpler and more reliable but may struggle with very large tasks.
- For long-duration tasks, consider using an LLM to compress action histories into key details.
- Claude Code uses subtasks carefully to avoid context overflow and conflicting responses.
- Edit Apply Models were unreliable due to misinterpretations; now, single models handle edits better.
- Multi-agent collaboration is currently fragile due to dispersed decision-making and poor context sharing.
- Future advancements in single-threaded agents may unlock better parallelism and efficiency.
- Agent-building principles are evolving, requiring flexibility and humility as the field advances.