Agentic software development represents a shift in how engineering teams approach product delivery. Rather than using AI for code suggestions or autocomplete, AI agents take on autonomous task execution from receiving specifications to verifying results.
Over the course of several projects using this approach, our team has developed a systematic workflow with templates, guardrails, and feedback loops that let AI agents produce production-ready code. With each project, we refine our approach, but are now to the point where we can share our process and what we've learned.
This guide covers the architecture and practices that make agentic development fast, effective, and reliable.
Agentic software development uses AI agents to handle portions of the software development lifecycle autonomously. An agent receives a specification, breaks it into subtasks, writes tests, implements code, and verifies the results with minimal human direction.
The traditional delivery process remains largely intact with discovery workshops, requirements gathering, solution design, and UX flows. But once you have a clear product definition, agents can take over significant portions of the implementation while humans focus on architecture, review, and problem-solving.
Here's the workflow for implementing this approach on a project. This AI agent orchestration process is iterative, not linear. When agents fail or produce unexpected results, you may need to return to an earlier stage to adjust the template, refine specifications, or rethink the approach.
Product design remains human-led, though AI tools can assist. This stage includes initial sales conversations, collaborative workshops to define scope, high-level architectural planning, and UX/UI mockups. The output is a clear product requirements definition (PRD) that agents can work from.
A specification agent formalizes the product definition into actionable technical documentation. The agent extracts details from Figma, tailors specifications to fit the template framework, creates data models and API designs, and defines product requirements. The output is a set of markdown files and YAML specifications that coding agents can reference.
Before implementing these specifications, humans validate that they are ready for the coding agent. This involves reviewing their accuracy, checking how well template features match the specs, and generating specific implementation tasks. If gaps exist, return to earlier stages to address them.
The coding agent implements features using test-driven development (TDD). It draws on rulesets, examples, integrations, and guardrails from the template. For each task, the agent writes a failing test, implements code until it passes, and then updates the task status and run log. Humans review the code after each task or batch of tasks.
Humans verify the results through prompt engineering, general QA checks, and UX review to identify improvements. If issues surface, the process loops back and you might return to specification generation to clarify requirements, or to the template itself to add missing guardrails or features.
Building products this way requires a dedicated framework, or template, before AI agents can produce reliable code. The template contains the architecture, guardrails, and rules that constrain how the AI works.
A useful mental model is to know that a perfect template combined with a perfect specification would let an agent build a product entirely on its own. That ideal is not achievable today, but the closer your template gets, the less manual intervention you need.
Templates address several things that AI agents struggle with on their own. Infrastructure patterns such as data access, authentication flows, and background processing are pre-built. Integration test frameworks give agents a way to verify their own work. Architectural rules prevent agents from inventing inconsistent patterns across the codebase.
Good templates follow a few architecture principles: they support multiple infrastructure providers to avoid lock-in, they prioritize simplicity so AI agents can understand and work within them, and they detect architectural drift through automated tests.
Organizing the project as a monorepo with templates as subtrees gives agents the context they need in one place. The agent can access backend, frontend, and infrastructure code without switching between repositories, while subtree separation keeps unrelated files from cluttering its working memory.
More importantly, templates enforce constraints the AI cannot override. If you define personal data that requires authentication, the underlying infrastructure uses the signed-in user's identity regardless of what the agent writes in the API layer. Even if the AI makes wrong assumptions about user IDs or authentication, the template's data access classes ignore those mistakes and use the correct identity from the system.
Security issues that would be easy to introduce in a traditional codebase become nearly impossible. The agent can attempt to expose personal data through a public endpoint, but the template architecture prevents it.
One common problem with AI coding tools is that agents overwrite fixes you've already made. You correct something, move on to the next task, and later discover the agent has refactored your correction back to the broken version.
A run log solves this. It's a file in the repository that documents every decision a human has made: what was changed, why, the git commit hash, and the date. Agent instructions require reading the run log before starting any work. When a new agent pulls down the repository, it inherits all previous decisions and can apply similar reasoning to new situations. This matters because AI agents lack persistent memory — by externalizing decisions into a structured log, you give agents access to project history they would otherwise lose between sessions.
For agents to work autonomously, they need a way to verify their own output. Test-driven development with comprehensive integration testing provides that feedback loop.
The TDD cycle works like this: the agent receives a task and breaks it into subtasks, writes a test that should fail (red), implements code until the test passes (green), then updates the task status and run log. The full test suite must pass before moving to the next task.
Integration tests are particularly important because they exercise the complete system: API calls hit a real database, authentication flows use actual identity providers, responses match expected schemas. Unit tests alone would not catch the integration issues that derail AI-generated code.
For frontend development, the feedback loop requires browser automation. Tools like Playwright with model context protocol (MCP) integration let agents interact with the UI as an end user would by clicking buttons, filling out forms, and navigating between screens. The agent can verify that functionality works, then take screenshots to check visual alignment against Figma designs.
Without this feedback loop, you get code that looks plausible but fails in unpredictable ways. With it, agents can iterate toward working solutions without constant human oversight.
AI agents are surprisingly creative at producing code that passes tests without solving the problem. One pattern to watch for is agents writing tests that validate a fallback value, then writing code that always returns that fallback. Everything passes. Nothing works.
These AI guardrails prevent this and other failure modes.
Architecture tests verify that code follows established patterns and does not violate structural rules. If the AI tries to bypass the template's conventions, automated tests catch it.
Constructive exceptions are error messages that tell the agent what went wrong and where to look. They prevent rabbit holes where the AI keeps trying the same broken approach. For example, when multiple integration tests run simultaneously and cause timing-dependent failures, a constructive exception tells the agent not to modify business logic and instead look at concurrent data access.
Terminology consistency means calling the same thing by the same name everywhere. If you call something an "apple" in one place and a "fruit" in another, agents will create inconsistent implementations. Enforce naming conventions through documentation and code review.
CI/CD enforcement through pre-commit hooks and pipeline checks catches issues before they reach the repository, including supply chain attacks where an agent installs a malicious npm package. Automated checks prevent compromised code from being merged or deployed.
Template-level constraints let the underlying infrastructure ignore incorrect agent decisions. Authentication, data access, and security rules operate independently of what the agent writes in application code.
One counterintuitive insight we had while continuing to work with agentic AI was that agentic AI increases the importance of senior engineering expertise rather than reducing it.
AI models have more raw knowledge than any individual developer. They have processed documentation for every framework, seen patterns from millions of codebases, and can generate syntactically correct code in any language. But they lack the contextual understanding to know when their output is wrong.
When an agent fails — and it will fail — you need to understand why. You need to diagnose whether the problem is in the specification, the template rules, or the agent's interpretation. You need to decide whether to fix it through better prompts, updated guardrails, or template improvements.
This requires being more experienced than the agent at the specific task. If you do not understand how something should work, you cannot identify why the AI's implementation does not.
A senior engineer using agentic AI effectively delivers significantly more than they would alone. But an engineer without the experience to catch AI mistakes may produce code that looks complete but fails in production.
Several findings have shaped our approach.
You can write explicit instructions: "you must do this, you must not do that," and at some point, the agent will do it anyway. Rules alone do not work. You need enforcement through template architecture, automated tests, and CI/CD checks.
Agents cannot hold an entire large codebase in memory. They work well on isolated features where the relevant files fit within their context. For features that span many files or require understanding complex interactions, you need to provide explicit guidance about which code is relevant.
If you want to test how far you can push AI automation, try starting with minimal instructions and see where the agent fails. We encountered a data migration framework that the agent could not use correctly despite extensive examples, step-by-step instructions, and detailed rules. The solution was building a simpler migration framework designed for AI comprehension and hiding all references to the original. Sometimes the fix is not better prompts but a simpler infrastructure.
Agentic software engineering works best for products that can be cleanly isolated. A mobile app with a CMS backend, well-defined screens, clear data models, and standard authentication flows is a good fit. The specification is bounded. The integration points are predictable. The template can cover most infrastructure needs.
The approach struggles with distributed systems. When you have multiple codebases, domain events flowing between services, complex orchestration, and high concurrency requirements, the context becomes too large. Documentation about system interactions does not fit in the agent's working memory. The AI cannot reason about timing, eventual consistency, or failure modes across service boundaries.
Products benefit from being designed with AI implementation in mind. Simpler data models, consistent terminology, and clear separation between features make specification generation more reliable and reduce the surface area for agent mistakes.
Our team has built this methodology through direct experience across multiple projects. The templates, guardrails, and workflows described here are actively used in our delivery work.
Senior engineers using these systems deliver more than traditional approaches would allow by combining deep technical expertise with AI capabilities that amplify their output. The methodology continues to improve as each project adds to the template library and refines the guardrails.
Interested in exploring agentic AI for your development projects? Talk to our team or learn more about our AI services.