Designing an autonomous research-and-report agent

WHAT THIS TESTS Whether you understand the standard agent loop and can justify each component rather than gesture at just use an LLM.

A GOOD ANSWER COVERS PLANNING. The planner converts the research goal into an ordered set of subtasks, for example gather sources, extract claims, synthesize, draft, verify. Plan-and-execute generates the plan upfront and revises it; ReAct interleaves reasoning and acting step by step. For research, a hybrid plan-then-react works well. MEMORY. Short-term memory is the active context window plus a scratchpad of intermediate notes and the current plan state. Long-term memory is an external vector store holding retrieved passages, deduplicated findings, and citations, queried by semantic similarity so the agent recalls earlier evidence without overflowing context. TOOL USE. Expose tools as structured functions with typed arguments: web search, page fetch and parse, and a citation logger. Validate outputs and cap result size. ERROR CORRECTION. Feed every tool result, including failures, back as an observation. On a bad or empty result the agent reflects and retries with a refined query, switches tools, or re-plans. Before writing, a verification step checks that each report claim is backed by a stored source.

COMMON WRONG ANSWERS Stuffing everything into one prompt. No long-term memory, so the agent forgets earlier findings. Treating tool errors as fatal instead of feedback. No verification, so the report contains unsupported claims. Unbounded loops with no step or cost limit.

LIKELY FOLLOW-UPS How do you bound cost and prevent infinite loops? How does the agent decide when research is sufficient? How do you keep citations faithful? How do you handle contradictory sources?

ONE CONCRETE EXAMPLE Goal: report on solid-state batteries. The planner lists subtasks. The agent issues a web search; one query returns nothing, so it reflects and broadens the query. It stores retrieved abstracts in the vector memory with citations. During synthesis it queries memory for each subtopic, drafts a section, then the verifier confirms each sentence maps to a stored source, flagging one unsupported claim for a follow-up search before the final report is emitted.