One agent isn't enough
6 days ago
- #Context Engineering
- #AI Agents
- #Parallel Processing
- Agentic coding suffers from variance due to the stochastic nature of LLMs, leading to inconsistent performance across runs.
- Context engineering aims to shift the probability distribution of LLM responses to improve reliability and quality.
- Parallel agent runs help explore multiple solution paths, increasing the chance of finding optimal outcomes.
- Parallel agents provide benefits like multiple independent samples, different starting points, and validation through repetition.
- Two primary workflows for parallel convergence: generating multiple solutions and gathering complementary information.
- Example use case: debugging a modal rendering issue by exploring different technical perspectives.
- Intelligence-gathering agents can scan git history, documentation, code paths, and web research for comprehensive insights.
- Convergence of agent outputs indicates validated solutions, often leading to simpler and more effective results.
- Drawbacks include higher token usage, context bloating, and increased time for agent runs.
- Parallel convergence is best suited for complex tasks, while single agents suffice for simpler, well-defined problems.