Introduction
The most impactful capability you can give an AI agent is access to the web. Without it, the agent is limited to its training data — a frozen snapshot of the world that becomes more stale every day. With web search, the agent can access current information, verify facts, and ground its responses in real sources. In this article, we explore the technical details of how this works.
The Knowledge Cutoff Problem
Every LLM has a knowledge cutoff date. Events, products, prices, and facts that emerged after this date simply do not exist in the model's world. This creates several failure modes:
- Outdated information: "What is OpenAI's latest model?" might return a model from 2024
- Confident hallucination: The model generates plausible but incorrect answers about recent events
- Refusal: "I do not have information about events after [date]"
Web search solves all three of these by providing the model with current, factual information.
Architecture: How Agents Use Search
The Tool-Use Framework
Modern LLMs support function calling (also called tool use). The model can output a structured request to call a function, receive the function's output, and then use that output to generate its response.
# Simplified agent loop
messages = [system_prompt, user_message]
while True:
response = llm.generate(messages, tools=available_tools)
if response.has_tool_calls:
for tool_call in response.tool_calls:
result = execute_tool(tool_call)
messages.append(tool_call)
messages.append(tool_result(result))
else:
return response.text # Final answer
Search as a First-Class Tool
When we give an agent a search tool, we are essentially saying: "You can look things up on the internet whenever you need to." The agent decides when to use this tool based on the conversation context.
# Keiro search tools for an AI agent
tools = [
{
"name": "web_search",
"description": "Search the web for current information. Use for recent events, current prices, latest news, or anything that may have changed since your training.",
"endpoint": "https://kierolabs.space/api/search",
"parameters": {"query": "string"}
},
{
"name": "deep_research",
"description": "Perform multi-step research on a complex topic. Use when a simple search is not enough and you need a synthesized report from multiple sources.",
"endpoint": "https://kierolabs.space/api/research",
"parameters": {"query": "string"}
},
{
"name": "read_page",
"description": "Read the full content of a specific web page. Use when you have a URL and need the complete text.",
"endpoint": "https://kierolabs.space/api/web-crawler",
"parameters": {"url": "string"}
},
{
"name": "quick_answer",
"description": "Get a direct answer to a factual question with sources. Use for straightforward questions where you need a sourced answer.",
"endpoint": "https://kierolabs.space/api/answer",
"parameters": {"query": "string"}
}
]
When Agents Decide to Search
A well-designed agent does not search for everything. It makes intelligent decisions about when web search would actually help. Here are the common decision factors:
Temporal Signals
Words like "current," "latest," "today," "2026," or "now" signal that the user wants recent information. Agents learn to trigger search for these queries.
Specificity Signals
Questions about specific prices, stock values, weather, or scores need real-time data. The agent knows its training data cannot reliably answer these.
Uncertainty Signals
When the agent is not confident in its answer, it can verify by searching. This is a powerful self-correction mechanism.
Explicit Requests
When the user says "search for," "look up," or "find me," the agent should always search.
Multi-Step Search Strategies
Simple one-shot search works for basic questions, but complex tasks require more sophisticated strategies:
Strategy 1: Decompose and Search
# Agent breaks a complex question into sub-queries
user_question = "Compare NVIDIA and AMD's latest AI chips for inference workloads"
# Agent's internal reasoning:
# I need to search for:
# 1. NVIDIA's latest AI inference chip specs
# 2. AMD's latest AI inference chip specs
# 3. Benchmarks comparing them
# Using Keiro batch search (free) for efficiency
resp = requests.post("https://kierolabs.space/api/batch-search", json={
"apiKey": api_key,
"queries": [
"NVIDIA latest AI inference chip 2026 specifications",
"AMD latest AI inference chip 2026 specifications",
"NVIDIA vs AMD AI inference benchmark 2026"
]
})
Strategy 2: Search, Read, Verify
# 1. Broad search
search_results = keiro_search("quantum computing breakthrough 2026")
# 2. Read the most relevant result in full
full_article = keiro_crawl(search_results[0]["url"])
# 3. Verify key claims with a targeted search
verification = keiro_search("specific claim from the article")
Strategy 3: Use Research for Synthesis
# For complex topics, let Keiro handle the multi-step research
research = requests.post("https://kierolabs.space/api/research-pro", json={
"apiKey": api_key,
"query": "Current state of nuclear fusion commercial viability in 2026"
})
# Returns a synthesized report from multiple sources
Grounding: Why Sources Matter
The most important aspect of web-augmented agents is grounding. Every claim the agent makes should be traceable to a source. This serves several purposes:
- Trust: Users can verify information by clicking through to sources
- Accuracy: The agent is less likely to hallucinate when constrained to source material
- Liability: For enterprise applications, sourced answers provide an audit trail
- Debugging: When the agent gives a wrong answer, you can trace it to the source
Real-World Agent Architectures
Research Assistant Agent
A research assistant agent uses Keiro's /research endpoint as its primary tool, supplemented by /search for targeted lookups and /web-crawler for reading specific documents.
Coding Assistant with Web Access
A coding assistant agent searches for documentation, API references, and Stack Overflow answers. It uses /search for broad queries and /web-crawler to read specific documentation pages.
Business Intelligence Agent
A BI agent monitors market trends, competitor activity, and industry news using Keiro's /batch-search (free for background processing) and /search-pro for real-time queries.
Performance Optimization for Agent Search
| Optimization | Technique | Impact |
|---|---|---|
| Parallel tool calls | Execute multiple searches simultaneously | 2-3x faster for multi-search tasks |
| Batch searching | Use Keiro /batch-search for known query sets | Free, reduced API calls |
| Search + crawl pipeline | Only crawl the most relevant result | Saves crawl API calls |
| Smart caching | Keiro's automatic 50% cache discount | 50% cost reduction on repeats |
| Tool selection | Use /answer for simple questions | Eliminates LLM generation cost |
Common Pitfalls
- Over-searching: Agents that search for every message waste time and money. Implement search decision logic.
- Ignoring search results: Sometimes agents generate answers that contradict their search results. Strong system prompts help prevent this.
- Too many iterations: Set a maximum number of tool calls to prevent infinite loops.
- Stale context: In long conversations, earlier search results may be pushed out of the context window. Summarize and condense as needed.
Conclusion
Web search is the single most important tool you can give an AI agent. It transforms a static knowledge system into a dynamic one that stays current, verifies facts, and provides sourced answers. Keiro's diverse set of endpoints (/search, /research, /answer, /web-crawler) provides the right tool for every type of information need an agent might have, at a cost that makes unlimited web access practical.
Give your agents web access with Keiro at kierolabs.space. 200,000 requests for $24.99/month.