OpenSearch Details 'Plan-Execute-Reflect' Agent Pattern
OpenSearch has published a guide for implementing the "plan-execute-reflect" agent pattern in Python. The approach decomposes complex tasks into a planning stage for goal breakdown, an execution stage for LLM-driven actions, and a reflection stage for self-assessment. This pattern is presented as a method for automating complex research or data processing tasks while maintaining oversight.
- The "plan-execute-reflect" pattern is conceptually similar to the Plan-Do-Check-Act (PDCA) cycle, a long-standing method for continuous improvement developed by W. Edwards Deming in the 1950s. The "reflection" stage is a key evolution, enabling the agent to perform self-correction by evaluating its own output, similar to a "Producer-Critic" model where one process generates work and another assesses it for quality and accuracy. - In quantitative finance, agentic AI systems are being integrated with frameworks like LangChain and AutoGen to automate tasks such as factor model development, time-series forecasting, risk scenario analysis, and derivatives pricing. The goal is to create autonomous agents that can dynamically create, validate, and backtest financial models in real-time. - OpenSearch's implementation uses a large language model (LLM) as a "planner" to devise a multi-step strategy and another LLM as an "executor" to carry out each step using a conversational agent. This allows the agent to dynamically adjust the plan by adding, skipping, or changing steps based on the intermediate results of previous actions. - This agent pattern is particularly suited for complex research tasks that were previously difficult to automate, such as querying a vector database with financial documents for a RAG task, analyzing the retrieved data, and then generating a summarized report based on the findings. - The AI agents in OpenSearch can be configured with a portfolio of "tools," including built-in functions for data retrieval and analysis, as well as connections to external data sources through the Model Context Protocol (MCP). This allows a developer to create agents that can, for example, query an internal OpenSearch index of market data and then call an external API for sentiment analysis scores. - For a quantitative developer, this pattern can be used to build systems that automate parts of the trading workflow, such as using an agent to monitor market data, identify potential arbitrage opportunities based on a predefined plan, execute a series of orders via broker APIs, and then reflect on the execution quality and slippage to refine future actions. - The general availability of these agentic features was part of OpenSearch version 3.3, released in October 2025. The framework allows developers to use various external LLMs, with tutorials demonstrating integration with models like Anthropic's Claude 3.7 hosted on Amazon Bedrock.
