— PROJECT
Autonomous Research Agent
An 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.
THE CONCEPT
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.
ARCHITECTURE
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.
THE HARD PARTS
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.
STATUS
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.