DEV Community: Emy Wilson

AI Coding Agents in 2026: What They Can Do and Where Developers Still Need to Step In

Emy Wilson — Thu, 22 Oct 2026 11:48:58 +0000

Artificial intelligence is changing how software is built. Developers can now use AI tools to generate code, explain unfamiliar functions, identify bugs, and automate parts of the software development process. The next step is the rise of AI coding agents, which can handle multiple development tasks instead of simply suggesting a line of code.
In 2026, coding agents are becoming an important part of modern development workflows. They can inspect project files, plan changes, modify code, run tests, and respond to errors. However, greater autonomy does not automatically mean better software.
Understanding what AI coding agents can do, where they struggle, and how developers should supervise them is essential for using these tools effectively.

What Are AI Coding Agents?

An AI coding agent is a software development tool that uses an AI model to work toward a specific programming goal. Unlike a traditional code-completion assistant, an agent can often perform several connected tasks with less continuous direction from the developer.
For example, a developer might ask an agent to fix a failing test. Depending on its tools and permissions, the agent may inspect the relevant files, identify the likely cause, update the implementation, run the test again, and explain the changes.
This process generally involves four stages:
Understanding the task: Interpreting the developer's instructions and examining relevant project context.
Planning the work: Breaking a larger request into smaller steps.
Taking action: Editing files, running commands, or using available development tools.
Checking the result: Examining test output, identifying errors, and refining the solution.
The exact capabilities depend on the agent, the model, the development environment, and the permissions it receives. An agent should not be assumed to perform every step reliably without supervision.

AI Coding Assistants vs. AI Coding Agents

Although the terms are sometimes used interchangeably, there is a practical difference between a coding assistant and a more autonomous coding agent.

Capability	Traditional AI coding assistant	AI coding agent
Code suggestions	Suggests functions or snippets	Can generate and modify code across files
Bug fixing	Explains errors or proposes fixes	Can investigate and attempt fixes
Project context	Often focuses on selected code or conversation context	Can inspect broader project context when permitted
Testing	May suggest test cases	Can run available tests and react to results
Task execution	Usually requires more step-by-step direction	Can coordinate multiple steps toward a goal

These categories overlap. Modern coding assistants increasingly include agent capabilities, so the distinction is about how much work the tool can coordinate and execute, rather than the product's name.

5 Practical Ways Developers Can Use AI Coding Agents

1. Automating Repetitive Coding Tasks

Many development tasks follow predictable patterns. Creating basic functions, updating repetitive code, writing documentation, and applying consistent formatting can consume valuable time.

A coding agent can help handle these tasks when given clear instructions and access to the relevant files.

For example, a developer maintaining several API endpoints might ask an agent to add consistent input validation to each endpoint. The agent can propose or apply changes across the project, while the developer checks that the implementation follows the application's existing conventions.

The goal is not to remove developers from the process. It is to reduce repetitive work so they can focus on more complex engineering decisions.

2. Debugging Errors and Investigating Bugs

Debugging is another useful application of AI coding agents. Instead of asking for an explanation of an error alone, a developer may delegate the investigation of a failing test or unexpected application behavior.

An agent with appropriate tool access can examine error messages, trace relevant code paths, propose a fix, and run the available tests.

However, passing one test does not prove that a bug has been fully resolved. A change might fix the immediate error while creating a different problem elsewhere.

Developers should therefore ask agents to explain the suspected root cause, identify affected components, and verify the fix with relevant test cases.

3. Generating and Improving Automated Tests

Testing is essential, but writing comprehensive tests can be time-consuming. AI coding agents can help create unit tests, suggest edge cases, and identify scenarios that existing tests might overlook.

Consider a function that calculates a shopping discount. A basic test might check a standard discount, but a more complete test suite should also consider:

A zero-value purchase.
A discount at the maximum permitted limit.
An invalid discount percentage.
Rounding and currency precision.
Missing or unexpected input values.

An agent can propose these cases and generate test code. Developers still need to confirm that the expected results reflect the application's actual business rules.

This is particularly important when software handles payments, customer information, or other sensitive operations.

4. Understanding Unfamiliar Codebases

Developers regularly work with projects they did not originally build. Understanding how files, services, dependencies, and APIs fit together can take considerable time.

When an agent can access the relevant repository, it may help explain how a feature works, locate the implementation of a particular function, or map the flow of data between components.

A useful starting prompt is:

Explain how this feature works from the initial request to the final response. Identify the main files involved, the dependencies between them, and any assumptions I should verify. Do not modify any files yet.

Starting with analysis rather than immediate code changes helps the developer understand the proposed approach before allowing the agent to make modifications.

5. Supporting Code Refactoring and Documentation

As projects grow, code can become repetitive, difficult to maintain, or inconsistent with current conventions. AI coding agents can help identify opportunities for refactoring and update documentation alongside code changes.

For example, an agent may propose extracting duplicated logic into a reusable function or updating outdated instructions after an API changes.

The important word is propose. A shorter implementation is not always a better implementation. Refactoring should preserve expected behavior, maintain readability, and be supported by tests.

Where AI Coding Agents Still Need Human Oversight

The ability to execute multiple steps makes coding agents useful, but it also increases the importance of reviewing their work.

Incorrect Assumptions

AI agents can misunderstand requirements or infer behavior that was never specified. A solution may look reasonable while failing to address the real problem.

What developers should do: Provide explicit requirements, expected outputs, constraints, and examples of correct behavior.

Security Vulnerabilities

Generated code can contain insecure input handling, weak authorization checks, unsafe dependencies, or accidental exposure of sensitive information.

What developers should do: Review security-sensitive changes, run appropriate security checks, and avoid granting agents unrestricted access to secrets, production systems, or critical infrastructure.

Changes That Break Other Features

An agent may solve a problem in one component while unintentionally changing the behavior of another. This is especially likely in projects with complex dependencies or incomplete test coverage.

What developers should do: Review the code diff, run relevant tests, and test important workflows before merging changes.

Excessive Trust in Automated Testing

An agent may report that tests pass, but the tests themselves might be incomplete or incorrect. A successful test run only establishes that the executed tests passed under the conditions tested.

What developers should do: Check whether the tests cover the actual requirements, including edge cases, failure scenarios, and relevant integration behavior.

Uncontrolled Tool Access

Some agents can interact with terminals, repositories, external services, and other development tools. Poorly defined permissions can turn a small mistake into a much larger problem.

What developers should do: Restrict access to what the task requires, isolate risky operations where practical, and require approval for destructive or high-impact actions.

A Practical Workflow for Using AI Coding Agents Safely

A structured workflow can make AI-assisted development more reliable without eliminating its productivity benefits.

Step 1: Define the outcome.

Describe the problem, expected behavior, relevant constraints, and how success will be measured. Avoid vague instructions such as “make the application better.”

Step 2: Ask for a plan.

Have the agent explain which files it expects to change, what approach it intends to take, and what risks it has identified. Review the plan before authorizing significant modifications.

Step 3: Limit permissions.

Give the agent only the access needed for the task. Use a controlled development environment and avoid exposing production credentials or sensitive data unnecessarily.

Step 4: Execute and test.

Allow the agent to make the approved changes and run appropriate checks. Ask it to report which tests were executed, which passed, and which remain unresolved.

Step 5: Review the changes.

Inspect the diff rather than relying solely on the agent's summary. Look for unexpected modifications, missing error handling, security issues, and changes that exceed the original request.

Step 6: Approve the final result.

Merge or deploy only after the required review and validation. Keep a way to revert changes if a problem appears later.
This approach treats the agent as a capable development assistant while keeping responsibility for software quality with the people maintaining the project.

AI Agents Are Also Changing Other Digital Workflows

The broader shift toward AI-assisted automation extends beyond software engineering. Digital platforms often rely on organized data, automated processes, and systems that help users find relevant information efficiently.

For example, an online savings platform such as alloCodepromo.fr operates in a digital environment where organizing promotional information and helping consumers discover relevant offers are important parts of the user experience. These activities are different from coding, but both illustrate why reliable data processing and well-designed digital workflows matter.

The lesson is not that every online platform needs an autonomous AI agent. It is that automation works best when it addresses a clear problem, produces verifiable results, and remains aligned with users' needs.

Will AI Coding Agents Replace Software Developers?

AI coding agents are likely to change how developers spend their time, but replacing the entire software development role is a much broader challenge than generating code.

Software engineering involves understanding business requirements, choosing system architecture, balancing competing constraints, investigating unusual failures, protecting user data, and maintaining systems over time. These responsibilities require judgment that cannot be measured simply by how quickly code is produced.

As agents become more capable, developers may spend less time on routine implementation and more time on task definition, architecture, verification, security, and coordination.

This also changes the skills that matter. Developers who understand debugging, testing, system design, and code review are better positioned to evaluate AI-generated work rather than accepting it blindly.

The most useful question is therefore not whether AI will write all the code. It is how developers can use these systems to deliver reliable software while retaining control over important decisions.

Final Thoughts

AI coding agents are becoming a practical part of software development by helping automate repetitive work, investigate bugs, generate tests, and coordinate changes across projects. Their value depends on more than their ability to produce code: they must also work within clear requirements, appropriate permissions, and effective validation processes.

For developers, the best approach is to combine automation with critical thinking. Let an agent handle suitable tasks, but review its assumptions, verify its output, and take responsibility for the final implementation.

AI can help developers move faster, but reliable software still depends on knowing what to build, how to test it, and when not to trust an automated result.

How Technology Is Making Online Price Comparison Easier

Emy Wilson — Mon, 19 Oct 2026 05:52:51 +0000

Online shopping has made it easier than ever to explore products, compare brands, and find competitive prices. However, choosing the best deal is not always as simple as looking at two price tags.

The same product may appear on multiple websites with different prices, delivery fees, availability, and promotional offers. Checking every store manually can take time, especially when shopping for electronics, home appliances, clothing, or other products with many alternatives.

Technology is helping solve this problem. From automated product matching to price-tracking tools and e-commerce APIs, modern systems make it easier to collect information, compare offers, and make informed purchasing decisions.

In this article, we will explore how online price comparison technology works, the tools behind it, and how shoppers can use it more effectively.

1. How Online Price Comparison Technology Works

Price comparison platforms collect product information from different sources and organize it in a format that shoppers can understand.

Depending on the platform, information may come from retailer-provided product feeds, APIs, authorized data partners, or other collection methods.

A typical comparison process involves several steps:

Data collection: Gathering product details, prices, availability, and retailer information.
Product matching: Identifying listings that refer to the same product.
Price normalization: Converting prices into a consistent format and currency where necessary.
Offer comparison: Presenting prices alongside relevant details such as delivery costs and stock status.
Data updates: Refreshing information so users can make decisions based on current offers.

These steps require more work than simply displaying a list of numbers. If a system compares different product versions or uses outdated information, its recommendations may be misleading.

Reliable price comparison therefore depends on both data quality and the algorithms used to organize it.

2. The Role of APIs in E-Commerce

Application Programming Interfaces, commonly known as APIs, allow different software systems to exchange information through defined interfaces.

In e-commerce, APIs can provide structured product information, including names, identifiers, prices, stock availability, and product links.

For example, a shopping application could request product information from a supported data service and display available offers in a single interface.

This reduces the need to collect and organize every piece of information manually.

However, API access does not automatically guarantee complete or real-time data. Coverage, update frequency, rate limits, and permitted uses depend on the provider and its agreements with retailers.

Developers building price comparison applications must therefore choose appropriate data sources and clearly communicate when prices were last updated.

3. How Algorithms Identify the Same Product

One of the biggest challenges in online price comparison is determining whether two listings represent the same product.

Consider a laptop listed on three different websites. One retailer might use the full model name, another might shorten it, and a third might emphasize its processor or memory configuration.

A basic text search may treat these listings as different products even when they refer to the same model.

Product-matching systems can use several signals:

Product identifiers such as GTINs, UPCs, or EANs.
Manufacturer model numbers.
Brand and product names.
Technical specifications and product attributes.
Text similarity and other matching techniques.

More advanced systems can combine multiple signals to improve matching accuracy.

This matters because two products that look similar may have different specifications. A laptop with 8GB RAM should not automatically be treated as identical to a version with 16GB RAM, even if the product names are almost the same.

Accurate matching helps shoppers compare equivalent products rather than simply choosing the lowest number on the screen.

4. Price Tracking Makes Shopping More Strategic

Traditional price comparison shows the offers available at a particular moment. Price tracking adds another useful dimension: time.

A price-tracking tool records price changes and helps users understand how an item's cost has changed.

Depending on the service, shoppers may be able to:

View historical price information.
Set alerts for a target price.
Monitor selected products.
Identify potential discounts.
Avoid making decisions based only on a temporary sale label.

For example, a product advertised as discounted may not represent exceptional value if its price was lower several weeks earlier.

Historical data can provide useful context, although tracking coverage and historical records vary between platforms.

Price alerts can also reduce the need to revisit product pages repeatedly. Instead of checking manually every day, shoppers can wait for a notification when a tracked price meets their chosen conditions.

5. How Coupon Codes Fit Into the Price Comparison Process

Product prices are only one part of the final purchase cost. Promo codes, delivery fees, taxes, and other charges can change which offer represents the best value.

This is where coupon platforms complement price comparison tools.

For example, platforms such as dealabs.com and allocodepromo.fr help shoppers discover the latest promo codes and discount offers. A coupon can potentially reduce the final price of an eligible purchase, but its value depends on the retailer's terms and the products included in the promotion.

A practical shopping process is to:

Identify the exact product you want.
Compare its price across relevant retailers.
Check delivery charges and availability.
Look for eligible promotional codes.
Review the final checkout total before placing an order.

Suppose one store lists a product for €100 with free delivery, while another lists it for €95 with a €7 delivery charge. The second option costs €102 before any eligible discount, so the lower advertised price does not necessarily represent the better deal.

A valid promotional code could change the result, which is why comparing the final cost is more useful than focusing on the headline price alone.

6. Artificial Intelligence Is Improving Product Discovery

Artificial intelligence is creating additional opportunities for shopping and price comparison tools.

AI-powered systems can help interpret natural-language searches, organize product descriptions, summarize specifications, and recommend products based on stated preferences.

For example, a shopper might search for a lightweight laptop suitable for programming within a specific budget. A capable shopping assistant could help narrow the options by comparing memory, processor specifications, battery life, and price.

However, recommendations are only as reliable as the data and criteria behind them. Incomplete product information, incorrect matches, and outdated prices can still lead to poor results.

Shoppers should also understand how recommendation systems use personal information and whether commercial relationships influence the results they see.

AI can make product discovery more convenient, but it does not eliminate the need to verify specifications, retailer terms, and final prices.

7. Common Limitations of Price Comparison Tools

Although technology has simplified comparison shopping, no tool should be assumed to cover every retailer or offer.

Several limitations deserve attention.

Outdated information: A price may change after a comparison service last updated its data.

Different product versions: Similar names can hide important differences in specifications, size, condition, or included accessories.

Hidden costs: Delivery charges, taxes, and additional fees may not appear in the initial listing.

Limited retailer coverage: Some services include only selected stores or marketplaces.

Promotional restrictions: Coupon codes may expire, require a minimum purchase, or apply only to selected products.

To reduce these problems, check the retailer's current listing, confirm the exact product model, review the offer conditions, and verify the final checkout total.

Conclusion

Technology has made online price comparison faster and more accessible by automating data collection, matching products, tracking price changes, and organizing offers in one place.

APIs help systems exchange structured product information, algorithms improve product matching, and AI can make product discovery more intuitive. Price-tracking tools and coupon platforms add further context that helps shoppers evaluate available offers.

However, the cheapest advertised price is not always the cheapest final purchase. Product specifications, delivery fees, availability, and promotional conditions all matter.

By combining technology with careful verification, shoppers can make more informed decisions, spend less time searching, and choose offers that provide genuine value.

As e-commerce continues to evolve, accurate data and transparent comparisons will remain essential to a better online shopping experience.