Research tasks are among the best applications for AI agents. They involve gathering information from multiple sources, synthesizing findings, and producing structured output - exactly the kind of multi-step, tool-using work that agents excel at. This guide walks through building a research agent that demonstrates key patterns: hierarchical delegation to specialists, parallel execution for speed, and memory for maintaining context across the research process.
What Makes a Good Research Agent
Effective research requires several distinct capabilities. Finding relevant sources across different information types - web pages, academic papers, news articles, documentation. Reading and extracting key information from those sources. Analyzing findings to identify patterns, contradictions, and gaps. Synthesizing everything into coherent, well-organized output.
A single agent attempting all of this becomes unwieldy. The prompt becomes long and conflicting. The tool set becomes cluttered. The agent struggles to switch between different modes of operation.
A better approach uses multiple specialized agents. Each specialist handles one capability well. An orchestrator coordinates them, breaking the research task into phases, delegating appropriately, and combining results.
This hierarchical structure keeps each agent focused while enabling complex research workflows that would overwhelm a single agent.
The Architecture
The research agent system has four components:
The orchestrator understands research tasks, breaks them into appropriate sub-tasks, delegates to specialists, and integrates results. It does not do detailed research, analysis, or writing itself - it coordinates.
The search specialist finds relevant sources. It has access to web search, academic databases, and news sources. Its job is returning high-quality, relevant sources for a given topic.
The analysis specialist processes sources and extracts insights. It reads documents, identifies key claims and evidence, notes patterns and contradictions, and produces structured analysis.
The writing specialist creates the final output. It takes research and analysis and produces well-organized, clearly written content with appropriate citations.
Each specialist is simpler than a general-purpose agent. Focused prompts, focused tools, focused behavior.
The Search Specialist
The search specialist needs tools for finding information and a prompt that guides effective search behavior.
Tools might include web search for general information, academic paper search for scholarly sources, and news search for recent developments. Each tool type finds different kinds of sources.
The prompt should emphasize quality over quantity. Five excellent sources beat fifty mediocre ones. Guidance on evaluating source credibility helps the specialist distinguish authoritative sources from unreliable ones. Instructions on search strategy - trying different query formulations, searching across source types - improve coverage.
The specialist's output is a structured list of sources with URLs, summaries, and relevance notes. This structured format makes the output useful for downstream specialists.
The Analysis Specialist
The analysis specialist takes sources and extracts meaningful insights.
Tools might include document reading to access full content, summarization for processing long documents, and possibly computation tools for data-heavy analysis.
The prompt should emphasize critical thinking. What does each source claim? What evidence supports those claims? Do sources agree or contradict? What patterns emerge across sources? What questions remain unanswered?
The output is structured analysis - key findings organized by theme, noted agreements and disagreements, identified gaps, and assessment of evidence strength. This structured format feeds cleanly into writing.
The Writing Specialist
The writing specialist creates polished output from research and analysis.
Tools might be minimal - perhaps formatting tools or grammar checking. The specialist's value is in synthesis and clear communication, not in using many tools.
The prompt should emphasize clear structure, appropriate audience targeting, and proper use of sources. Guidance on citation formats helps maintain scholarly standards. Instructions on avoiding common writing problems - excessive jargon, unclear structure, unsupported claims - improve output quality.
The Orchestrator
The orchestrator coordinates the specialists to complete research tasks.
The orchestrator's prompt explains what specialists are available and what each does well. It provides guidance on breaking research tasks into phases. It explains how to delegate effectively - providing enough context for each assignment without overwhelming specialists with irrelevant information.
The orchestrator should maintain a mental model of the research progress. What has been gathered? What has been analyzed? What remains? Memory helps track this across potentially long research sessions.
For delegation, the orchestrator uses sub-agents as tools. Calling the search specialist with a research topic assignment. Receiving results. Calling the analysis specialist with those sources. Receiving analysis. Calling the writing specialist with research and analysis. Receiving the final output.
Parallel Execution
Research tasks often involve multiple independent information needs. Researching a topic might require understanding historical context, current state, and future trends - three distinct searches that do not depend on each other.
Sequential execution means three searches take three times as long. Parallel execution means all three run simultaneously, completing in the time of the longest individual search.
The orchestrator can enable this by delegating to multiple specialists at once. When the searches are independent, the infrastructure runs them in parallel. Results return together when all complete.
This parallel capability is particularly valuable for broad research topics. Searching five different aspects of a topic in parallel rather than sequentially can reduce research time from minutes to seconds.
Memory and State
Research accumulates information over time. Early searches inform later questions. Analysis reveals gaps that require additional searches. The research process is iterative, not linear.
Memory enables this iteration. The orchestrator can store key findings as they emerge, building up a picture of what has been learned. When deciding what to research next, the orchestrator consults memory to avoid duplicating work and to identify remaining gaps.
For long research sessions, memory persistence across message turns enables the agent to build on previous work rather than starting fresh each time the user asks a follow-up question.
Running the Research Agent
With the architecture in place, using the research agent is straightforward. A user provides a research topic and any specific requirements. The orchestrator breaks this into phases, delegates to specialists, and coordinates the results.
The user might see status updates as work progresses - searches starting, sources found, analysis beginning, writing in progress. When complete, the user receives a structured research report with findings, analysis, and citations.
For complex topics, the user can ask follow-up questions. The memory of what has already been researched enables building on previous work rather than starting over.
Extending the Pattern
The basic research agent can be extended in several directions.
More source types expand where the agent can search. Adding specialized databases, documentation sources, or domain-specific repositories improves coverage for particular research areas.
Quality review adds a specialist that checks research output for accuracy, completeness, and clarity. This catches issues before delivery.
User feedback incorporates human guidance into the research process. Approval gates before final delivery let users request adjustments.
Domain specialization adapts the agents for specific fields. A legal research agent might have different sources and analysis patterns than a technical research agent.
For teams building research capabilities into their applications, inference.sh provides the infrastructure for multi-agent research systems. Sub-agents are native tools. Parallel execution is automatic. Memory is built in. You design the research workflow; the runtime handles the coordination.
Research agents demonstrate what becomes possible when agents can coordinate, specialize, and work in parallel. The same patterns apply to many complex tasks beyond research - content creation, data processing, analysis pipelines. The research agent is a template for any multi-phase, multi-capability workflow.
FAQ
How do I handle research topics where good sources are hard to find?
Source scarcity is a real challenge. The search specialist should be designed to try multiple search strategies rather than giving up after one failed attempt. Broadening queries, trying related terms, searching different source types, and using question-based queries all increase the chance of finding relevant material. When sources remain scarce, the analysis should acknowledge this explicitly rather than pretending confidence. The writing specialist can frame findings as preliminary given limited sources. Transparency about source limitations is better than false confidence based on thin evidence.
How long should a research agent be allowed to run before delivering results?
There is no universal answer - it depends on the research scope and user expectations. Simple questions might complete in under a minute. Comprehensive research on complex topics might take ten minutes or more. Communication is key: show users what is happening so they understand progress is being made. Set reasonable timeouts to prevent indefinitely long runs. Consider offering tiered research depth - quick summary versus comprehensive analysis - letting users choose the appropriate level for their needs.
Can research agents cite sources properly?
Yes, but it requires deliberate design. The search specialist should capture source URLs and metadata. The analysis specialist should track which findings came from which sources. The writing specialist should include citations using appropriate formats. The challenge is maintaining source attribution through the pipeline - a finding in the final output should be traceable to the source where it was found. Structured output formats at each stage help maintain these connections. Some implementations add citation validation as a final step.