Research Agents

Research agents are AI applications that work through research tasks in multiple steps, such as finding sources, comparing information and preparing a report. Their tools and assignment determine which data and research activities they can handle.

Research Agents explained

Start by turning a broad question into an assessable brief: which decision, market and time period does the work address? An agent can investigate subquestions and respond to new findings. A longer report does not necessarily provide better understanding.

Research using existing sources differs from primary research. An agent reviewing competitor websites has not interviewed customers. A generated questionnaire is not a completed field study; an interview summary replaces neither the interview nor a justified sample.

Assessment depends on connecting each claim to its supporting material. Keep company statements, independently collected data and your own conclusions distinguishable. Preserve contradictions and gaps instead of smoothing them into a confident narrative.

In Creative Engineering, AI can structure information work and create room for better questions and interpretation. We assess quality and economics through usable results, including verification effort. We take responsibility for the concept and quality.

Examples

Hypothetical application

A team compares packaging claims in retail. A research agent records statements from a defined set of product pages, with sources and dates, and flags missing substantiation. This provides material for strategy work; it does not establish which packaging consumers prefer.

Key Points

The assignment and data access define the scope.
Distinguish desk research from new data collection.
Check the source against the specific claim.
Keep unanswered questions visible.

Practical application

Define questions, source scope and output format first. Require a supporting passage for decision-relevant claims and separate observation, interpretation and recommendation. Have the result assessed before using it to justify a decision.

Useful measures

Substantiation

Decision-relevant claims with appropriate, verified supporting evidence.

Coverage

Answered subquestions and remaining gaps.

Effort to usable output

Research, verification and correction required for decision support.

Common mistakes

Presenting a sourced assertion as an independently collected finding.
Equating source volume with evidential strength.
Measuring generation time alone.

Sources and context

Anthropic: How we built our multi-agent research system
Technical example of multi-step research; its internal benchmarks are not a general forecast of results.

Frequently Asked Questions about Research Agents

The label alone does not establish that. Individual activities may be supported or automated. A credible study still needs a suitable design, appropriate data and expert interpretation.

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Research Agents

Research Agents explained