Case Study: Research Agent
A production case study for a research agent that searches, reads, synthesizes, and cites across large information sets.
Prerequisites
- 1Familiarity with RAG, context engineering, and evaluation
- 2Helpful: Workflow Reliability and Multimodal GenAI coverage
What you will learn
- How to structure a research agent around search, reading, synthesis, and citation
- Where retrieval, summarization, and human review fit in the workflow
- How to avoid unsupported synthesis and citation drift
- What to measure in research-agent quality beyond answer fluency
Problem Shape
A research agent is valuable when the task is not just “find one fact,” but “search broadly, compare sources, synthesize findings, and produce a usable report.” This is a workflow problem as much as a model problem.
Architecture
A common research architecture has four stages:
question
-> search plan
-> retrieval and reading
-> note-taking and evidence compression
-> synthesis with citations
The crucial design choice is keeping evidence gathering separate from synthesis. If those blur together, unsupported claims become hard to detect.
Operational Controls
Good research agents should:
- preserve source references through the full pipeline
- distinguish retrieved evidence from generated synthesis
- limit summary drift by versioning intermediate notes
- support human review for high-importance reports
Useful metrics include citation accuracy, source diversity, unsupported-claim rate, and time to first credible draft.
Failure Modes
Research-agent failures usually look like:
- over-summarizing early and losing nuance
- citing the wrong passage for a claim
- preferring fluent synthesis over source-grounded synthesis
- treating stale or low-quality sources as equivalent to better ones
Common Mistakes to Avoid
- !Combining retrieval, synthesis, and final writing into one opaque model step
- !Dropping citations during note compression and then trying to reconstruct them later
- !Measuring only final answer fluency instead of citation quality and evidence coverage
- !Overusing one source or one domain and calling the result research
- !Skipping human review for external or high-stakes reports
Recommended Next Steps
Explore Related Content
RAG & Agentic RAG
Retrieval-augmented generation and its evolution into agentic systems with hierarchical retrieval.
GuideEvaluation & Testing
Test agent performance with unit tests, integration tests, benchmarks, and regression suites.
ConceptMultimodal GenAI
How GenAI systems work across text, images, audio, video, and documents instead of text alone.