The Top Use Cases for Agentic Development in 2026

The conversation around AI coding assistants has matured rapidly. We have moved past "will AI write code?" to "where does AI write code most effectively?"

The answer is not everywhere. AI agents excel at specific categories of work—often work that humans find tedious, time-consuming, or economically unviable. The highest ROI comes from deploying agents strategically rather than broadly.

After working with dozens of Canadian companies on agentic development initiatives, we have identified the use cases that consistently deliver value.

1. Legacy Codebase Refactoring

This is the killer app for agentic development.

Every mature organization has legacy systems. Code written in older frameworks. Business logic scattered across files with no documentation. Dependencies that have not been updated in years. Technical debt that everyone acknowledges but nobody has time to address.

Human developers hate this work. It is tedious, risky, and rarely celebrated. Refactoring a 50,000-line PHP monolith to modern standards could take a team months—months that leadership would rather spend on new features.

AI agents change the economics entirely.

An agent can methodically work through a codebase, understanding patterns, updating syntax, modernizing dependencies, and applying consistent style—at a fraction of human cost and without the morale drain.

Where We See This Applied:

Framework migrations: Rails 4 to Rails 7, Angular.js to React, Python 2 to Python 3
Dependency updates: Systematically updating packages across large codebases
Code style standardization: Applying consistent formatting, naming conventions, and patterns
Documentation generation: Creating docstrings and README files for undocumented code
Test coverage expansion: Writing unit tests for legacy code that shipped without them

The key insight: agents do not get bored. They will apply the same careful attention to the 500th file as the first. This unlocks refactoring projects that were previously too tedious to staff.

2. Internal Tools and Admin Interfaces

Every company has internal tools they wish were better.

The admin dashboard that only half works. The reporting tool built five years ago that nobody maintains. The data entry interface that makes employees groan. Internal systems accumulate because they are never the priority—customer-facing features always win the sprint.

Agentic development is perfect for this backlog.

Internal tools have relaxed requirements compared to customer-facing products. They do not need pixel-perfect design. They do not need to scale to millions of users. They need to work well enough for internal staff who will forgive rough edges if the tool saves them time.

High-Value Internal Tool Projects:

Custom CRM interfaces tailored to your specific sales process
Reporting dashboards that pull from multiple data sources
Data entry forms with validation and workflow automation
Admin panels for managing application configuration
Integration utilities that connect systems which do not natively talk to each other
Onboarding tools that automate new employee setup across systems

An agent can build a functional internal tool in hours that would take a human developer days. More importantly, it unlocks tools that would never get built at all because they could not compete for engineering resources.

3. Rapid Experimentation and Prototyping

Speed to learning is a competitive advantage.

The faster you can test an idea, the faster you can validate or discard it. Traditional development cycles—sprint planning, implementation, review, deployment—optimize for quality but sacrifice speed.

Agentic development enables a different approach: generate working prototypes rapidly, learn from real usage, then decide whether to invest in production-grade implementation.

The Experimentation Workflow:

Describe the feature or product concept in plain language
Agent generates a working prototype in hours
Deploy to a small user group for feedback
Learn whether the concept has value
If validated, invest in proper implementation; if not, move on

This is not about shipping agent-generated code to production (though that is sometimes appropriate). It is about collapsing the time between "I wonder if users would want X" and "Here is data on whether users want X."

Where This Shines:

Product discovery: Testing feature concepts before committing engineering resources
Sales demos: Building custom prototypes for enterprise sales conversations
Investor pitches: Demonstrating product vision with working software
User research: Creating functional prototypes for usability testing
Hackathons: Enabling teams to build more ambitious projects in limited time

4. Test Generation and Quality Assurance

Writing tests is important. Writing tests is also tedious.

Most development teams know they should have better test coverage. Most development teams ship features without adequate tests because the deadline pressure is real and tests feel like a tax on velocity.

AI agents can invert this dynamic.

Given a codebase, an agent can generate comprehensive test suites—unit tests, integration tests, edge case coverage—at a pace humans cannot match. More importantly, agents will write the boring tests: the null checks, the boundary conditions, the error handling paths that humans skip because they are tedious.

Testing Applications:

Retroactive test coverage: Adding tests to legacy code that shipped without them
Edge case generation: Systematically testing boundary conditions humans overlook
Regression test creation: Building tests from bug reports to prevent recurrence
API contract testing: Generating tests that verify API behavior matches documentation
Cross-browser testing scripts: Creating test suites that verify behavior across environments

The result is not just better coverage—it is confidence to refactor and improve code without fear of breaking changes.

5. Documentation and Knowledge Capture

Documentation is a classic "important but not urgent" task. It gets deprioritized until someone leaves the company and takes critical knowledge with them.

Agents excel at documentation generation because they can read code and produce explanations at scale.

Documentation Use Cases:

API documentation: Generating OpenAPI specs and usage examples from code
Architecture diagrams: Creating visual representations of system structure
Onboarding guides: Producing documentation for new team members
Code comments: Adding inline documentation to complex logic
Runbooks: Generating operational documentation for production systems
Knowledge base articles: Creating searchable documentation from code and commit history

The key is that agent-generated documentation is a starting point, not a final product. Humans review and refine, but the heavy lifting of initial creation is automated.

6. Compliance and Security Remediation

Security vulnerabilities and compliance gaps share a common characteristic: they accumulate faster than teams can address them.

A security scan might identify 200 issues across a codebase. A compliance audit might flag 50 areas needing remediation. Addressing these manually competes with feature development for engineering time—and usually loses.

Agents can systematically work through remediation backlogs:

Dependency vulnerabilities: Updating packages with known CVEs
SQL injection fixes: Rewriting queries to use parameterized statements
XSS remediation: Adding proper escaping to user-controlled output
Secrets rotation: Identifying hardcoded credentials and moving them to proper secret management
Logging improvements: Adding audit trails for compliance requirements
Access control updates: Implementing consistent authorization patterns

This is not about blindly applying fixes—human review remains essential. But agents can prepare the changes, dramatically reducing the time security engineers spend on mechanical remediation.

7. Boilerplate and Scaffolding

Every new feature, service, or component starts with boilerplate: project structure, configuration files, base classes, standard patterns.

Experienced developers often maintain personal templates. Mature organizations build internal scaffolding tools. But these solutions require maintenance and often drift out of date.

Agents can generate boilerplate on demand, tailored to your specific codebase patterns:

New service scaffolding: Creating microservices with your standard structure
Feature templates: Generating the files needed for a new feature across your stack
Configuration generation: Creating environment-specific config files
CI/CD pipelines: Building deployment configurations for new projects
Infrastructure as code: Generating Terraform or CloudFormation from requirements

The advantage over static templates: agents can understand context. They can examine your existing code and generate new code that matches your patterns, not generic best practices.

The Pattern Across Use Cases

Notice what these use cases have in common:

High volume, repetitive work that humans find tedious
Clear correctness criteria that agents can verify
Lower risk tolerance than customer-facing features
Work that otherwise would not happen due to resource constraints

Agentic development is not about replacing human developers on their core work. It is about unlocking work that was previously too expensive, too tedious, or too low-priority to attempt.

The organizations winning with agentic development are not asking "can AI do what my developers do?" They are asking "what could we accomplish if we had infinite patience for tedious work?"

Ready to identify agentic development opportunities in your organization? We help Canadian companies deploy AI coding agents safely and effectively. Let's assess your highest-ROI use cases.