As intelligent agents become increasingly widespread, a host of advanced technologies—such as Agentic RL and MCP—are being employed to enhance their capabilities. Throughout our work building agentic systems, we have identified several key challenges and build Flame to address them:

- Session-based isolation
- MCPs and agents discovery
- Session-level context of user for RL
- On-demand parallelism for multi-path reasoning

In this presentation, we showcase the "Simple Research Agent" (SRA) as a concrete example of how Flame and MCP solve these challenges. SRA is a multi-agent system that generates concise research reports. The supervisor agent formulates a plan based on the user prompt; the collector agent analyzes the prompt to gather additional context using sources like DuckDuckGo and web crawlers; finally, the writer agent employs large language models (LLMs) to synthesize and refine the report for the user.