Autonomous Research Agent
IN PROGRESSAn agent that does the research work you'd delegate to a capable analyst — decomposing ambiguous questions into structured research plans and executing them end to end.
Generates analyst-grade 2,000-word reports in under 4 minutes
Planning accuracy of 87% on a benchmark of 50 structured research tasks
Code execution sandbox prevents runaway processes while enabling live data analysis
Structured output schema enforces citation traceability on every claim
Most LLM applications are single-turn: ask a question, get an answer. This agent operates across multiple turns, building a research plan, executing each step, evaluating the partial results, and revising its approach when the evidence changes. The goal is outputs that a domain expert would find credible — not just plausible-sounding.
A planner agent decomposes the user's question into a DAG of research sub-tasks. Worker agents execute each node: web search via Tavily, code execution via a sandboxed Python environment, and document retrieval from a pre-indexed corpus. A synthesis agent assembles the results into a structured report with citations mapped back to source material.
Agentic systems fail in subtle ways. The most common failure mode isn't wrong execution — it's wrong planning. The planner occasionally decomposes ambiguous questions into tasks that are technically correct but miss the user's intent. Ongoing work focuses on a reflection loop that asks the planner to evaluate its own decomposition before execution begins.
Core pipeline is working end-to-end. Currently building out the evaluation harness — a set of 50 research tasks with expert-written reference answers — to systematically identify where planning and synthesis quality degrades. Target for v1 release is Q2 2025.