Creating AI-Powered Research Agents Using Claude

Why Research Agents Are Harder Than They Look

Most AI agent tutorials make everything look straightforward. Give the model a tool, write a system prompt, loop until done. Ship it.

Research is different because the problem space is fundamentally open-ended. When you ask an agent to "research the competitive landscape for edge ML inference platforms," there's no single correct answer. There's no test you can run to verify the output. The quality depends entirely on the methodology the agent follows, the sources it finds, the judgments it makes about relevance and reliability, and how it synthesizes conflicting information.

This is why pointing Claude at a topic and saying "research this" produces mediocre results. You're asking it to simultaneously figure out what to research, how to research it, what counts as a good source, when it has enough information, and how to organize the output. That's five different cognitive tasks jammed into one vague instruction.

The solution is the same one that works for human researchers: break it down into distinct phases, each with clear objectives and quality criteria.

The Research Agent Architecture

A production research agent isn't one agent. It's a pipeline of specialized stages, each handling one aspect of the research process. Here's the architecture that actually works:

Stage 1: Question Decomposition

The first agent takes the research question and breaks it into sub-questions. This is where most people skip straight to searching, and it's exactly where the quality gap opens up.

A good decomposition agent doesn't just split a question into parts. It identifies:

• Factual sub-questions that have definitive answers (market size, founding dates, technical specifications)
• Analytical sub-questions that require synthesis (competitive positioning, trend analysis, strategic implications)
• Gap-identification questions that surface what we don't know yet (emerging competitors not yet covered by analysts, unpublished technical limitations)

Stage 2: Parallel Search and Collection

Once you have sub-questions, you fan out. Each sub-question gets its own search agent that hunts for relevant sources. This is where parallelism pays off—five searches running simultaneously instead of sequentially.

But here's the hidden layer: raw search results are not research. They're ingredients. The collection stage needs to capture not just the content, but metadata about each source—when it was published, who wrote it, what their perspective or bias might be, whether the source is primary or secondary.

Stage 3: Source Evaluation

This is the stage most agent builders skip entirely, and it's the one that separates useful research from noise. Each collected source gets evaluated on:

• Recency: Is this information current enough to be relevant?
• Authority: Is the source credible for this specific claim?
• Corroboration: Do other independent sources support the same claim?
• Bias: Does the source have a financial or ideological stake in the claim?
• Specificity: Does the source provide concrete evidence or just vague assertions?

Stage 4: Synthesis and Cross-Referencing

Now we actually do the research. The synthesis agent takes evaluated sources and builds a coherent picture, explicitly noting where sources agree, where they conflict, and where gaps remain.

Stage 5: Confidence Assessment and Output

The final stage doesn't just format the output. It assigns confidence levels to each finding and flags areas where the research is thin. This is critical—research that doesn't tell you what it doesn't know is worse than useless because it gives you false confidence.

Building the Decomposition Agent

Let's get concrete. Here's how you build the question decomposition stage using Claude's API:

import anthropic
import json
 
client = anthropic.Anthropic()
 
DECOMPOSITION_PROMPT = """You are a research methodology expert. Your job is to take
a research question and decompose it into a structured research plan.
 
For each sub-question, classify it as:
- FACTUAL: Has a definitive, verifiable answer
- ANALYTICAL: Requires synthesis of multiple data points
- EXPLORATORY: Open-ended, may surface unexpected findings
 
Output a JSON object with this structure:
{
  "original_question": "the input question",
  "research_scope": "1-2 sentence boundary statement",
  "sub_questions": [
    {
      "id": "sq_1",
      "question": "the sub-question",
      "type": "FACTUAL|ANALYTICAL|EXPLORATORY",
      "search_strategy": "how to find this information",
      "quality_criteria": "how to evaluate if we found a good answer",
      "dependencies": ["sq_ids this depends on, if any"]
    }
  ],
  "out_of_scope": ["things we are explicitly NOT researching"]
}
"""
 
def decompose_research_question(question: str) -> dict:
    """Break a research question into a structured research plan."""
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=4096,
        system=DECOMPOSITION_PROMPT,
        messages=[
            {
                "role": "user",
                "content": f"Decompose this research question into a structured "
                           f"research plan:\n\n{question}"
            }
        ]
    )
 
    # Parse the JSON from Claude's response
    response_text = response.content[0].text
    # Extract JSON from potential markdown code fences
    if "```json" in response_text:
        response_text = response_text.split("```json")[1].split("```")[0]
    elif "```" in response_text:
        response_text = response_text.split("```")[1].split("```")[0]
 
    return json.loads(response_text.strip())
 
 
# Example usage
plan = decompose_research_question(
    "What are the leading edge ML inference platforms, their technical "
    "trade-offs, and which are gaining market traction in 2026?"
)
 
print(f"Generated {len(plan['sub_questions'])} sub-questions")
for sq in plan["sub_questions"]:
    print(f"  [{sq['type']}] {sq['question']}")

Notice what this does that a naive approach doesn't: it forces the agent to think about how to research before it starts researching. The search strategy and quality criteria fields mean each sub-question carries its own methodology. The dependencies field lets you build a DAG of research tasks, so analytical questions wait for the factual questions they depend on.

The Multi-Agent Research Pipeline

Now let's wire the full pipeline together. This is where the decompose-search-evaluate-synthesize pattern comes to life:

import anthropic
import asyncio
import json
from dataclasses import dataclass, field
from enum import Enum
 
client = anthropic.Anthropic()
 
class ConfidenceLevel(Enum):
    HIGH = "high"        # Multiple corroborating sources, recent data
    MEDIUM = "medium"    # Some corroboration, minor gaps
    LOW = "low"          # Single source, outdated, or conflicting info
    UNKNOWN = "unknown"  # Insufficient data to assess
 
@dataclass
class ResearchFinding:
    claim: str
    evidence: list[str]
    sources: list[dict]
    confidence: ConfidenceLevel
    conflicts: list[str] = field(default_factory=list)
    gaps: list[str] = field(default_factory=list)
 
@dataclass
class ResearchReport:
    question: str
    findings: list[ResearchFinding]
    methodology_notes: str
    confidence_summary: dict
    known_gaps: list[str]
 
 
def search_for_subquestion(sub_question: dict) -> dict:
    """Search agent: finds and collects sources for a single sub-question.
 
    In production, this integrates with web search APIs, internal
    knowledge bases, or document stores. Here we use Claude with
    web search tool to ground findings in real sources.
    """
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=4096,
        system="""You are a research search specialist. For the given
research sub-question, find relevant information and return structured
results. For each piece of information found, note:
- The specific claim or data point
- Where you found it (source name, URL if available, date)
- How authoritative this source is (1-5 scale)
- Whether this is a primary source or secondary reporting
 
Return as JSON with structure:
{
  "sub_question_id": "id",
  "results": [
    {
      "claim": "specific finding",
      "source": {"name": "", "url": "", "date": "", "type": "primary|secondary"},
      "authority_score": 4,
      "raw_excerpt": "relevant quote or data"
    }
  ],
  "search_notes": "what you searched for and any difficulties"
}""",
        messages=[
            {
                "role": "user",
                "content": (
                    f"Research this sub-question:\n\n"
                    f"Question: {sub_question['question']}\n"
                    f"Type: {sub_question['type']}\n"
                    f"Search strategy: {sub_question['search_strategy']}\n"
                    f"Quality criteria: {sub_question['quality_criteria']}"
                )
            }
        ]
    )
    text = response.content[0].text
    if "```json" in text:
        text = text.split("```json")[1].split("```")[0]
    return json.loads(text.strip())
 
 
def evaluate_and_synthesize(
    original_question: str,
    search_results: list[dict]
) -> ResearchReport:
    """Synthesis agent: cross-references findings, resolves conflicts,
    assigns confidence levels, identifies gaps."""
 
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=8192,
        system="""You are a research synthesis expert. You receive raw search
results from multiple sub-questions and must:
 
1. CROSS-REFERENCE: Identify claims supported by multiple independent sources
2. CONFLICT RESOLUTION: Flag where sources disagree and assess which is
   more credible
3. GAP ANALYSIS: Identify what we still don't know
4. CONFIDENCE SCORING: Rate each finding as HIGH, MEDIUM, LOW, or UNKNOWN
   confidence based on source quality and corroboration
5. SYNTHESIS: Build a coherent narrative from the validated findings
 
Confidence criteria:
- HIGH: 3+ independent, authoritative, recent sources agree
- MEDIUM: 2 sources agree, or 1 highly authoritative source
- LOW: Single source, low authority, outdated, or conflicting reports
- UNKNOWN: Insufficient data
 
Return JSON:
{
  "findings": [
    {
      "claim": "synthesized finding",
      "confidence": "HIGH|MEDIUM|LOW|UNKNOWN",
      "supporting_evidence": ["evidence 1", "evidence 2"],
      "source_count": 3,
      "conflicts": ["any contradicting claims"],
      "gaps": ["what we still need to verify"]
    }
  ],
  "methodology_notes": "how the synthesis was conducted",
  "overall_confidence": {"high": N, "medium": N, "low": N, "unknown": N},
  "critical_gaps": ["most important things we don't know"],
  "recommendations": ["suggested follow-up research"]
}""",
        messages=[
            {
                "role": "user",
                "content": (
                    f"Synthesize these research results for the question: "
                    f"'{original_question}'\n\n"
                    f"Raw results:\n{json.dumps(search_results, indent=2)}"
                )
            }
        ]
    )
    text = response.content[0].text
    if "```json" in text:
        text = text.split("```json")[1].split("```")[0]
    synthesis = json.loads(text.strip())
 
    findings = []
    for f in synthesis["findings"]:
        findings.append(ResearchFinding(
            claim=f["claim"],
            evidence=f["supporting_evidence"],
            sources=[],
            confidence=ConfidenceLevel(f["confidence"].lower()),
            conflicts=f.get("conflicts", []),
            gaps=f.get("gaps", [])
        ))
 
    return ResearchReport(
        question=original_question,
        findings=findings,
        methodology_notes=synthesis["methodology_notes"],
        confidence_summary=synthesis["overall_confidence"],
        known_gaps=synthesis["critical_gaps"]
    )
 
 
def run_research_pipeline(question: str) -> ResearchReport:
    """Full research pipeline: decompose -> search -> synthesize."""
 
    # Phase 1: Decompose
    plan = decompose_research_question(question)
    print(f"Research plan: {len(plan['sub_questions'])} sub-questions")
 
    # Phase 2: Search (in production, parallelize with asyncio)
    all_results = []
    for sq in plan["sub_questions"]:
        print(f"  Searching: {sq['question'][:60]}...")
        result = search_for_subquestion(sq)
        all_results.append(result)
 
    # Phase 3: Synthesize
    print("Synthesizing findings...")
    report = evaluate_and_synthesize(question, all_results)
 
    # Phase 4: Output with confidence
    print(f"\nResearch complete:")
    print(f"  Findings: {len(report.findings)}")
    print(f"  Confidence: {report.confidence_summary}")
    print(f"  Known gaps: {len(report.known_gaps)}")
 
    return report

There's a lot happening here, so let's unpack the design decisions.

Separate agents for search and synthesis. This isn't just architectural neatness—it prevents a subtle failure mode where the model starts rationalizing weak sources to fill gaps in its narrative. When the search agent only searches and the synthesis agent only synthesizes, each can be evaluated independently. If the synthesis is poor but the sources are good, you know where to focus your improvement efforts.

Structured output at every stage. JSON schemas force the model to be explicit about things it would otherwise gloss over in prose—source authority, confidence levels, conflicts. When Claude writes a paragraph of synthesis, it can hide uncertainty behind smooth language. When it has to fill in a "confidence" field and a "conflicts" array, it can't dodge.

Confidence levels as a first-class concept. The output doesn't just tell you what the agent found. It tells you how much to trust each finding and what it still doesn't know. This is the difference between research and opinion.

Source Evaluation: The Stage Everyone Skips

Let me drill into source evaluation because this is where research agents either earn their keep or become expensive search wrappers.

The problem with naive search-and-summarize is that the agent treats all sources as equally valid. A blog post from someone who used a product once gets the same weight as a peer-reviewed benchmark study. A vendor's marketing page gets cited alongside independent analysis. An article from 2022 gets treated as current fact in 2026.

Your source evaluation prompt needs to be explicit about these traps:

SOURCE_EVALUATION_PROMPT = """Evaluate each source using the CRAAP framework:
 
Currency: When was this published? Is it current enough for this topic?
- Technology topics: >12 months old = flag as potentially outdated
- Market data: >6 months old = flag as potentially outdated
- Foundational concepts: age matters less
 
Relevance: Does this source directly address our specific question?
- Directly relevant (addresses exact question)
- Tangentially relevant (addresses related topic)
- Peripheral (mentions topic in passing)
 
Authority: Who created this content? What are their credentials?
- Primary source (the company/researcher themselves)
- Expert secondary source (analyst, journalist with domain expertise)
- General secondary source (news aggregator, blog)
- Anonymous/unknown authority
 
Accuracy: Can the claims be verified? Are they supported by evidence?
- Cites primary data or original research
- Makes specific, falsifiable claims
- Provides methodology or sourcing
- Makes vague or unsubstantiated claims
 
Purpose: Why does this source exist? What's the motivation?
- Inform/educate (academic, documentation)
- Analyze (research firms, expert commentary)
- Sell (vendor content, marketing)
- Persuade (opinion pieces, advocacy)
 
For each source, output:
{
  "source_id": "id",
  "craap_scores": {
    "currency": 1-5,
    "relevance": 1-5,
    "authority": 1-5,
    "accuracy": 1-5,
    "purpose": 1-5
  },
  "overall_reliability": 1-5,
  "use_recommendation": "CITE|USE_WITH_CAVEAT|CROSS_REFERENCE_ONLY|DISCARD",
  "bias_flags": ["any detected biases"],
  "notes": "evaluation reasoning"
}"""

This framework gives the synthesis stage actual data to work with. Instead of blindly merging all sources, it can weight high-reliability sources more heavily, flag claims that only come from biased sources, and explicitly note when a finding depends on a single uncorroborated reference.

Real-World Example: Competitive Intelligence Agent

Let me walk through how this architecture works on a real research task. Say you're building a competitive intelligence agent that monitors your market landscape.

The input question: "What are the emerging competitors to our vector database product, what are their technical differentiators, and which ones show the strongest growth signals?"

Decomposition produces these sub-questions:

1. [FACTUAL] What vector database products have launched or reached GA in the last 12 months?
2. [FACTUAL] What are the published benchmarks and technical specifications for each competitor?
3. [ANALYTICAL] How do these products differentiate from established players (Pinecone, Weaviate, Qdrant, etc.)?
4. [ANALYTICAL] What growth signals are visible—funding rounds, hiring patterns, GitHub stars trajectory, community activity?
5. [EXPLORATORY] Are there adjacent technologies (embedded vector search in existing databases, in-memory solutions) that could disrupt the standalone vector database market?

Search fans out across all five questions simultaneously. Question 1 hits product launch announcements, Hacker News, and ProductHunt. Question 2 pulls from documentation sites and benchmark repositories. Question 4 trawls funding databases and job boards.

Source evaluation catches critical issues. That glowing benchmark comparison? Published by one of the competitors—flagged as PURPOSE: Sell, USE_WITH_CAVEAT. The market size projection? From a 2024 report—flagged as outdated for CURRENCY. The GitHub stars data? Primary source, highly reliable.

Synthesis produces a structured report with 15 findings, each with confidence levels. Eight findings are HIGH confidence (multiple independent sources). Four are MEDIUM (two sources or single authoritative source). Three are LOW (single source or conflicting data). The report explicitly lists five gaps: missing benchmark data for two products, no public information on one company's funding, conflicting claims about another's architecture.

That LOW confidence and gaps section? That's the most valuable part. It tells you exactly what you still need to figure out through primary research—talking to customers, running your own benchmarks, making direct inquiries. An agent that only tells you what it found is less useful than one that also tells you what it couldn't find.

Patterns That Make Research Agents Better

After building several production research agents, here are the patterns that consistently improve output quality:

The Methodology-First Pattern

Don't start with "research X." Start with "here is how to research X." Give the agent a research methodology before giving it a research question. This flips the agent from reactive (search for whatever seems relevant) to systematic (follow this process to ensure comprehensive coverage).

The Devil's Advocate Pattern

After your synthesis agent produces findings, run a second pass with an adversarial prompt: "For each finding, identify the strongest counterargument or alternative interpretation. What would someone who disagrees with this finding say, and what evidence would they cite?" This catches confirmation bias—the tendency to accept the first plausible answer and stop looking.

The Iterative Deepening Pattern

Run the pipeline in two passes. First pass: broad coverage, identify the landscape. Second pass: deep dive on the most important or most uncertain findings from the first pass. This mimics how human researchers work—you start with a survey, then dig into the areas that matter most.

The Provenance Chain Pattern

For every claim in the final output, maintain a chain back to the original source. Finding -> Evidence -> Source -> Search Query -> Sub-question -> Original Question. When a stakeholder asks "where did this come from?", you can trace any claim back to its origin in seconds. This isn't just about trust—it's about debuggability. When your research is wrong (and it will be sometimes), provenance chains let you figure out where the process broke down.

The Temporal Awareness Pattern

Research has a shelf life. Build expiration dates into your findings. A competitive landscape from January is stale by March. A funding round from last week is still fresh. Your agent should tag each finding with a "confidence decay" estimate—how quickly this information is likely to become outdated. This is especially critical for competitive intelligence agents that run on a schedule.

Common Mistakes and How to Avoid Them

Mistake 1: Treating search results as research. Search finds raw material. Research is what you do with that material. If your agent stops at "here's what Google returned," it's a search agent, not a research agent. Fix this by requiring explicit evaluation and synthesis stages.

Mistake 2: No confidence calibration. When everything in the report has the same implicit confidence level, the reader can't distinguish between rock-solid findings and educated guesses. Force explicit confidence scoring with defined criteria.

Mistake 3: Ignoring what you don't know. The gaps in your research are often more important than the findings. If your agent can't tell you what it failed to find, it's hiding its limitations behind polished prose. Make gap analysis a required output.

Mistake 4: One-shot research. Complex questions can't be fully answered in a single pass. Build iterative deepening into your pipeline so the agent can go back and investigate areas where the first pass came up thin.

Mistake 5: No source diversity. If all your findings come from the same type of source (all vendor blogs, all news articles, all Reddit threads), you have a systematic blind spot. Build source diversity requirements into your search stage—require at least N different source types.

Mistake 6: Prompt-as-methodology. Writing "be thorough and objective" in your system prompt is not a methodology. It's a hope. Real methodology means specific steps, specific evaluation criteria, specific output requirements. The more structured your prompts, the more consistent your research quality.

Scaling Research Agents in Production

When you move from prototype to production, three things change:

Cost management. A full research pipeline might make 10-20 API calls per question. At scale, this adds up. Use Haiku for search and source evaluation (they're classification tasks that don't need Opus-level reasoning), and reserve Sonnet or Opus for decomposition and synthesis where nuanced judgment matters. This can cut costs by 60-70% without meaningful quality loss.

Caching and deduplication. If multiple research questions hit similar sub-topics, you're paying to research the same thing repeatedly. Build a findings cache keyed by sub-question similarity. When a new question decomposes into sub-questions that match cached findings (and those findings haven't expired), skip the search and reuse the existing evaluated results.

Quality feedback loops. Track which findings users actually cite, question, or override. Feed this back into your source evaluation weights. If users consistently dismiss findings from a particular source type, downweight that source type automatically. This is how your research agent gets smarter over time instead of repeating the same blind spots indefinitely.

Where This Is Heading

Research agents today are essentially implementing the methodologies of human researchers with AI speed and scale. The next step is agents that improve their own methodology based on outcomes—noticing which research strategies produce findings that hold up over time, which source types are consistently reliable for specific domains, and which synthesis approaches produce the most actionable output.

We're not there yet, but the architecture described in this guide—modular stages with structured interfaces between them—is designed to evolve. You can swap in better search tools, smarter evaluation criteria, more sophisticated synthesis prompts, all without rebuilding the pipeline. Each stage is independently testable and improvable.

The research agents that win won't be the ones with the fanciest AI. They'll be the ones with the best methodology. That's always been true of research, and AI doesn't change it—it just makes good methodology scale.

Build the process first. Then let the AI accelerate it.