From Information Retrieval to Task Execution: A Guide to Building AI Agents

1. From Conversational AI to Task-Executing AI

In the past, most AI tools we interacted with were primarily Q&A-based. A user inputs a query, and the model returns an explanation, suggestion, or code snippet based on its training data or retrieval results. While this has significantly improved information retrieval efficiency, there is still a gap before a task is “actually completed.”

For example, if a user asks, “How to read an Excel file in Python,” a traditional AI returns a sample code snippet. The user still needs to open an editor, install dependencies, and run and debug the code. However, if the AI can understand the intent, directly generate an executable script, run it, and return the result, the user experience upgrades from “consultation” to “delegation.”

Behind this capability evolution is the transition of AI systems from language understanding to tool calling and workflow orchestration. The industry typically refers to such systems as “AI Agents” or “Task-executing AI.” Instead of merely outputting text, they can call external APIs, operate software interfaces, access authorized data, and even link multiple systems to complete end-to-end business operations.

2. Typical Capabilities of Task-Executing AI

From a practical application perspective, the current capabilities of task-executing AI can be categorized as follows:

1. Information Aggregation and Structured Output

AI can receive various input formats—such as PDFs, web pages, meeting minutes, lecture notes, and long articles—and transform them into structured summaries, Q&A pairs, knowledge cards, or mind maps. For developers, these capabilities are particularly useful for organizing technical documentation, clarifying requirements, and building knowledge bases.

For instance, given a dozens-page API document, AI can extract the request methods, parameter lists, return fields, and error codes of all interfaces, outputting them as a Markdown table. This not only improves reading efficiency but also facilitates the subsequent generation of test cases or SDK wrapper code.

2. Software Operation and UI Automation

Another critical capability of task-executing AI is simulating human operations on software interfaces. Through visual understanding and action planning, AI can identify page elements and perform actions like clicking, inputting, selecting, scrolling, and submitting.

For example, in a travel scenario, a user might simply say, “Book me a flight from Beijing to Shanghai for tomorrow morning.” The AI can automatically open the booking page, filter flights, select cabin class, fill in passenger information, and finally pause at the payment confirmation page for the user to complete the transaction.

The engineering implementation of this capability typically relies on two types of technologies: page structure parsing and multimodal vision models. The former is suitable for web pages or clients with stable structures, while the latter is better for scenarios with frequently changing interfaces and no stable selectors. In practice, many systems combine both, using rules as a fallback and model generalization to improve the success rate.

3. Cross-System Workflow Orchestration

A more valuable scenario is when AI bridges multiple business systems to complete tasks that originally required manual switching across platforms. For example, a complete “meeting scheduling” process might involve: checking participants’ calendars, booking a conference room, sending invitation emails, and generating meeting minutes and action items. AI can automate this workflow by calling calendar APIs, email services, and document systems.

For developers, such scenarios are well-suited for implementation using general cloud services like cloud functions, message queues, and API gateways. The AI is responsible for understanding intent and generating the call plan, while the underlying execution is guaranteed by the engineering system to ensure reliability and traceability.

3. A Simplified Task Execution Example

Below is a simple Python example illustrating how to build a basic task execution workflow using a “Large Language Model + Tool Functions” approach. In this example, all external tools represent general capabilities and do not involve any specific brands.

import json
from typing import Callable
# Define the set of tools callable by AI
def search_flights(departure: str, arrival: str, date: str) -> dict:
    """Simulate flight search, returning structured results"""
    return {
        "flights": [
            {"no": "CA1234", "dep_time": "08:30", "arr_time": "10:45", "price": 980},
            {"no": "MU5678", "dep_time": "10:00", "arr_time": "12:15", "price": 860}
        ]
    }
def create_order(flight_no: str, passenger: str) -> dict:
    """Simulate order creation; real scenarios will call actual business APIs"""
    return {"order_id": "ORD20240601", "status": "pending_payment"}
TOOLS: dict[str, Callable] = {
    "search_flights": search_flights,
    "create_order": create_order
}
def run_agent(user_intent: str):
    """
    Simplified task execution flow:
    1. Generate a tool call plan based on user intent
    2. Execute tools and return results
    In real systems, step 1 is handled by an LLM; pseudo-code is used here.
    """
    # Assume the model outputs the following call plan
    plan = [
        {"tool": "search_flights", "args": {"departure": "Beijing", "arrival": "Shanghai", "date": "2024-06-02"}},
        {"tool": "create_order", "args": {"flight_no": "MU5678", "passenger": "Zhang San"}}
    ]
    results = []
    for step in plan:
        tool_name = step["tool"]
        args = step["args"]
        if tool_name in TOOLS:
            result = TOOLS[tool_name](**args)
            results.append({tool_name: result})
    return results
# Simulate the call
output = run_agent("Book me a flight from Beijing to Shanghai for tomorrow morning")
print(json.dumps(output, ensure_ascii=False, indent=2))

This example is intentionally minimal. Real systems must also handle the following issues:

• Permission Validation: The system must verify if the user has the necessary permissions before AI calls a tool.
• Parameter Validation: Tool inputs require dual validation at both the model and engineering layers.
• Failure Retry and Compensation: When a step fails, the system must decide whether to retry, skip, or roll back.
• Audit Logs: Record every operation for tracking and post-mortem analysis.

4. Voice Interaction and Human-like Execution

Some task-executing AI systems are beginning to incorporate voice capabilities. For instance, users can describe their needs in natural language, and the AI can generate conversational content using text-to-speech technology, automatically dialing offline service providers like hotels or restaurants to complete bookings or inquiries.

The technical pipeline for this capability generally includes:

1. Speech Recognition: Converting user speech to text.
2. Intent Understanding: Extracting key information like time, number of people, room type, and budget.
3. Dialogue Strategy: Dynamically adjusting responses based on the other party’s replies.
4. Speech Synthesis: Generating natural and fluent voice responses.
5. Status Callback: Syncing the booking result back to the user.

From an engineering perspective, such systems demand high real-time performance, voice naturalness, and dialogue fault tolerance. Especially in phone environments, background noise, accents, and interruptions can impact the success rate. Therefore, general technical solutions like noise reduction, echo cancellation, and streaming voice processing are typically required.

5. Knowledge Base and Authoritative Information Retrieval

Another crucial foundation for task-executing AI is knowledge retrieval capability. Instead of relying solely on the model’s parametric memory, these systems typically integrate with external knowledge bases, literature libraries, or enterprise private data. They retrieve relevant materials via vector search, keyword search, or hybrid search, and then let the model generate answers based on these materials.

These capabilities are highly suitable for the following scenarios:

• Technical Documentation Q&A: Quickly locating answers from internal wikis or API docs.
• Academic Research Assistance: Finding relevant papers from literature databases and generating literature reviews.
• Compliance Review: Verifying terms and operational standards against regulatory documents.
• Meeting Minutes Organization: Converting multiple meeting recordings into summaries and action items.

For developers, building such systems requires careful attention to the chunking strategy, index quality, and update mechanism of the knowledge base. If chunks are too small, the context will be incomplete; if too large, retrieval relevance suffers. It is usually necessary to dynamically adjust chunk sizes based on document type and periodically rebuild the index.

6. Security and Permission Design: A Crucial Aspect

When AI upgrades from “giving advice” to “getting things done,” security boundaries become paramount. A task execution system without permission control can cause irreversible consequences due to misinterpreting user intent.

From an engineering practice perspective, it is recommended to implement controls at the following three levels:

1. User Identity and Authorization

Before every tool call, the system should verify whether the current user is authorized to perform the operation. High-risk operations like creating orders, sending emails, or deleting data must require explicit authorization.

2. Action Tiering

Categorize tools by risk level. Low-risk operations can be executed automatically, such as checking the weather or searching documents. Medium-risk operations require user confirmation, like filling out forms or generating drafts. High-risk operations must undergo secondary verification or manual approval, such as payments or production deployments.

3. Comprehensive Logging

Every operation executed by the AI should record the complete input, output, tool call chain, and final result. This serves both troubleshooting purposes and compliance audit requirements.

7. Deployment Recommendations for Task-Executing AI

If you are considering introducing task-executing AI into your business or development workflow, you can start from the following directions:

1. Start with Low-Risk Scenarios: Let the AI handle information organization, document generation, and query-based tasks first. Gradually open up write operations after verifying stability.
2. Build a High-Quality Tool Layer: The upper limit of AI often depends on the tools it can call. Prioritize wrapping common business capabilities into standardized APIs or functions for the model to invoke.
3. Set Up Manual Confirmation Nodes: Inserting manual confirmation at critical steps enhances security and collects user feedback for model optimization.
4. Monitor Execution Quality: Track metrics like task success rate, average execution time, and failure reasons to continuously iterate on prompts, tool descriptions, and workflow design.

8. Conclusion

AI tools are evolving from “information providers” to “task executors.” The core of this transition lies not in the model outputting longer text, but in its ability to understand user intent, plan execution steps, call external tools, and complete closed-loop operations.

For developers, understanding the engineering architecture, capability boundaries, and security design of task-executing AI is more critical than chasing a specific tool. Only by deeply integrating model capabilities with business systems can the true potential of AI in automation, efficiency improvement, and experience optimization be fully unleashed.