Research Workflows with AI Agents

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What This Article Covers

• How to build research workflows with AI agents.
• How agents automate web search, source evaluation, and summarization.
• How to implement fact-checking and quality control.
• Real-world examples for topic research, market analysis, and literature review.
• Best practices for reliability, avoiding hallucinations, and security.

Introduction: Understanding Research Workflows with AI Agents

Research is time-consuming. You read sources, compare information, assess credibility, and write summaries. AI agents can automate this process: they search the web, read pages, evaluate sources, synthesize findings, and cross-check facts. With local AI, everything runs on your hardware without sending research data to cloud providers.

This article is for developers who want to build research workflows with AI agents. You should understand what AI agents are and how to run them locally with Ollama. Python basics are available on IRC-Coding.de.

Why Use Research Workflows with AI Agents?

Imagine you need to write a report on an unfamiliar topic. You spend hours with Google, Wikipedia, and academic papers, reading and comparing information before summarizing. An AI agent can do this in minutes: search, read, evaluate, summarize, and deliver a structured report with sources.

Research workflows become especially valuable when you regularly investigate new topics, need to evaluate many sources, or want reproducible research processes.

How Research Workflows with AI Agents Work

A research workflow is an agent loop that repeats these steps: understand the question, formulate search queries, run web search, read results, evaluate sources, extract facts, cross-check information, and summarize. The agent uses tools for web search and page retrieval, while the language model makes decisions.

The core principle: the agent is the researcher, the tools are the search engine and browser, and the model is the brain.

Who Should Read This Article?

• Researchers wanting to automate literature reviews.
• Journalists needing to quickly investigate new topics.
• Analysts building market or competitive analyses.
• Developers building research agents.

You should be comfortable with Python, AI agents, and Ollama.

Key Concepts for Research Workflows

• Research Agent - AI agent that conducts research. Use when: automating research tasks.
• Web Search Tool - Tool that executes search queries. Use when: helping the agent find sources.
• Source Evaluation - Assessing the credibility of a source. Use when: separating reliable from unreliable sources.
• Fact Cross-Check - Verifying facts across multiple sources. Use when: preventing hallucinations.
• AI Agents - Programs that solve tasks independently. Use when: building the foundation for research agents.
• Ollama - Local model server. Use when: powering the agent’s reasoning.
• Function Calling - Structured AI responses. Use when: enabling tool use.
• Hallucination - When the model invents false facts. Use when: understanding the main challenge with research agents.

Architecture of a Research Workflow

A typical research workflow consists of several phases:

1. Understand the question: The agent analyzes the question and formulates search queries.
2. Web search: The agent finds relevant sources.
3. Fetch pages: The agent downloads the most relevant pages.
4. Extract content: The agent extracts text from pages.
5. Evaluate sources: The agent assesses credibility and relevance.
6. Extract facts: The agent pulls facts from the sources.
7. Cross-check: The agent compares facts across multiple sources.
8. Summarize: The agent synthesizes the findings.
9. Generate report: The agent creates a structured report with citations.

Tools for Research Agents

Web Search

Several APIs work well for web search:

import requests

def search_web(query, num_results=5):
    # DuckDuckGo (free, no API key)
    response = requests.get(
        "https://html.duckduckgo.com/html/",
        params={"q": query}
    )
    # Parse results
    return parse_results(response.text)

# Alternatives:
# - SearXNG (self-hosted)
# - Tavily API (built for AI agents)
# - Google Custom Search (API key required)
# - Brave Search API

For self-hosting, SearXNG is recommended because it has no API costs and keeps searches private.

Page Retrieval

import trafilatura

def fetch_page(url):
    # Downloads the page and extracts text
    downloaded = trafilatura.fetch_url(url)
    text = trafilatura.extract(downloaded)
    return text

trafilatura is a Python library that downloads web pages and extracts the main content, filtering out navigation, ads, and footers.

Source Evaluation

def evaluate_source(url, content):
    prompt = f"""
Evaluate the credibility of this source:
URL: {url}
Content (excerpt): {content[:1000]}

Criteria:
1. Author/organization identified?
2. Publication date present?
3. Facts backed by evidence?
4. Objective tone?
5. Sources cited?

Respond in JSON format:
{{"credibility": "high|medium|low", "reason": "..."}}
"""
    response = call_ollama([
        {"role": "system", "content": "You are a source evaluation expert."},
        {"role": "user", "content": prompt}
    ])
    return parse_json(response)

Example 1: Topic Research

An agent that researches a topic and produces a report with sources.

def research_topic(topic):
    # Phase 1: Generate search queries
    queries = generate_search_queries(topic)

    # Phase 2: Web search
    all_results = []
    for query in queries:
        results = search_web(query)
        all_results.extend(results)

    # Phase 3: Fetch top results
    top_results = all_results[:10]
    pages = []
    for result in top_results:
        content = fetch_page(result["url"])
        if content:
            pages.append({
                "url": result["url"],
                "title": result["title"],
                "content": content
            })

    # Phase 4: Evaluate sources
    for page in pages:
        page["evaluation"] = evaluate_source(page["url"], page["content"])

    # Phase 5: Extract facts
    facts = []
    for page in pages:
        if page["evaluation"]["credibility"] in ["high", "medium"]:
            page_facts = extract_facts(page["content"], topic)
            facts.extend(page_facts)

    # Phase 6: Cross-check facts
    verified_facts = cross_check_facts(facts)

    # Phase 7: Generate report
    report = generate_report(topic, verified_facts, pages)
    return report

Generating search queries

def generate_search_queries(topic):
    response = call_ollama([
        {"role": "system", "content": "Formuliere 5 Suchanfragen für eine Recherche. Antworte als JSON-Array."},
        {"role": "user", "content": f"Thema: {topic}"}
    ])
    return parse_json(response)

Extracting facts

def extract_facts(content, topic):
    response = call_ollama([
        {"role": "system", "content": "Extrahiere Fakten zum Thema. Antworte als JSON-Array von Fakten."},
        {"role": "user", "content": f"Thema: {topic}\nInhalt: {content[:3000]}"}
    ])
    return parse_json(response)

Cross-checking facts

def cross_check_facts(facts):
    verified = []
    for fact in facts:
        # Check if the fact is confirmed across multiple sources
        confirmations = count_confirmations(fact, facts)
        if confirmations >= 2:
            verified.append({
                "fact": fact,
                "confirmations": confirmations,
                "status": "verified"
            })
        else:
            verified.append({
                "fact": fact,
                "confirmations": confirmations,
                "status": "unverified"
            })
    return verified

Generating a report

def generate_report(topic, facts, sources):
    response = call_ollama([
        {"role": "system", "content": "Schreibe einen strukturierten Bericht mit Quellenangaben."},
        {"role": "user", "content": f"""
Thema: {topic}
Verifizierte Fakten: {facts}
Quellen: {[{"url": s["url"], "title": s["title"]} for s in sources]}
"""}
    ])
    return response

Practical example 2: Market analysis

An agent that analyzes a market: competitors, trends, and prices.

def market_analysis(product_category):
    # Find competitors
    competitors = research_topic(f"Wettbewerber im Markt: {product_category}")

    # Research trends
    trends = research_topic(f"Aktuelle Trends: {product_category}")

    # Compare prices
    prices = research_topic(f"Preisvergleich: {product_category}")

    # Combine reports
    report = combine_reports([competitors, trends, prices])
    return report

Practical example 3: Literature review

An agent that finds scientific literature on a topic.

def literature_review(topic):
    # Search queries for academic sources
    queries = [
        f"{topic} site:arxiv.org",
        f"{topic} site:pubmed.ncbi.nlm.nih.gov",
        f"{topic} site:doi.org"
    ]

    # Execute search
    results = []
    for query in queries:
        results.extend(search_web(query))

    # Extract abstracts
    papers = []
    for result in results:
        content = fetch_page(result["url"])
        if content:
            abstract = extract_abstract(content)
            papers.append({
                "title": result["title"],
                "url": result["url"],
                "abstract": abstract
            })

    # Generate summary
    summary = call_ollama([
        {"role": "system", "content": "Erstelle eine Literaturübersicht."},
        {"role": "user", "content": f"Papers: {papers}"}
    ])
    return summary

Avoiding hallucinations

Hallucinations are the biggest challenge in research agents. The model invents facts that don’t appear in the sources.

Strategies to prevent hallucinations

• Only facts from sources: The agent should use only facts found in actual sources.
• Source attribution: Every fact must cite a source.
• Cross-verification: Facts should be confirmed in at least two sources.
• Confidence scores: The agent should report how certain it is about each claim.
• Express uncertainty: When unsure, the agent should say so explicitly.

def extract_facts_safe(content, topic):
    response = call_ollama([
        {"role": "system", "content": """
Extrahiere nur Fakten, die explizit im Text stehen.
Erfinde keine Fakten.
Wenn Du unsicher bist, sage "unsicher".
Jeder Fakt braucht eine Quellenangabe (Zitat aus dem Text).
Antworte als JSON-Array:
[{"fact": "...", "quote": "...", "confidence": "high|medium|low"}]
"""},
        {"role": "user", "content": f"Thema: {topic}\nInhalt: {content[:3000]}"}
    ])
    return parse_json(response)

Quality assurance

Metrics for research quality

• Number of sources: How many sources did the agent retrieve?
• Source credibility: How trustworthy are the sources?
• Fact coverage: How many facts were verified?
• Confidence: How certain is the agent about its findings?
• Contradictions: Are there conflicting facts?

def quality_report(facts, sources):
    return {
        "total_sources": len(sources),
        "high_credibility_sources": sum(1 for s in sources if s["evaluation"]["credibility"] == "high"),
        "verified_facts": sum(1 for f in facts if f["status"] == "verified"),
        "unverified_facts": sum(1 for f in facts if f["status"] == "unverified"),
        "average_confidence": calculate_average_confidence(facts)
    }

Security notes

• Rate limits: Respect rate limits from search APIs. See API keys.
• No sensitive data: Don’t research sensitive topics via cloud APIs. Use local AI instead.
• Audit logging: Log all research steps. See Audit logging.
• Prompt injection: Protect agents from prompt injection in web content. See Prompt injection protection.
• Guardrails: Use guardrails to prevent harmful research. See Configuring guardrails.

Common pitfalls

• Hallucinations: The agent invents facts. Use source attribution and cross-verification.
• Poor sources: The agent relies on unreliable sources. Use source evaluation.
• Infinite loops: The agent keeps searching. Limit the number of search steps.
• Context limits: Many sources exceed token limits. Use summary memory.
• Stale information: Web search returns outdated results. Use date filters.
• Bias: The agent favors certain sources. Use diverse search queries.

Further reading and resources on research workflows

• AI agents fundamentals - What AI agents are.
• AI agents with Ollama - Running agents locally.
• Function calling - Tool use fundamentals.
• Installing Ollama - Backend for agents.
• Local RAG - RAG as an alternative to web search.
• Prompt injection protection - Security for web content.
• Configuring guardrails - Guardrails for agents.
• Audit logging - Auditability.

Key takeaways:

• Research workflows automate web search, source evaluation, and summarization.
• The agent uses tools for web search and page fetching; the model makes decisions.
• Against hallucinations: stick to facts from sources, provide source attribution, and cross-verify.
• Practical examples: topic research, market analysis, literature review.
• Security: respect rate limits, enable audit logging, protect against prompt injection.

FAQ: Research Workflows with AI Agents - Common Questions

References and Further Reading

• Ollama Documentation - Official Ollama docs.
• trafilatura - Web content extraction.
• SearXNG - Self-hosted search engine.
• Tavily API - Search API for AI agents.

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