Beyond the Chatbot: How to Build Software Using AI Agents and Skills in 2026
Beyond the Chatbot: How to Build Software Using AI Agents and Skills
A few years ago, coding looked very different.
You opened an editor, created a file, and started typing. Every function, every edge case, every error message came from your head. Tools helped, but they did not think with you.
Today, many people first meet AI in the form of chat. You ask a question. It answers. You paste the code. Sometimes it works. Sometimes it breaks in confusing ways.
What is changing right now is not just code generation. It is a new way of working with AI called agent based coding. At the center of this approach is a simple idea: instead of writing everything yourself, you give agents structured responsibilities using something called skills.
This article explains what AI agents are, what agent skills mean, and how this changes the way we plan, design, and implement software. No deep coding knowledge required. Just fundamentals.
What is an AI Agent? (The Simple Version)
An AI agent has a specific goal, such as producing software, and is designed to help you achieve that goal.
If a standard LLM (Large Language Model) is a brilliant librarian, an AI Agent is a project manager with a library card.
💡 Note from the dev desk: I have been spending a lot of time at the Edmonton Public Library lately, taking advantage of everything from their book stacks to their audio recording studios to record a few songs of mine. That's probably why this analogy hit home for me while writing this - it's the difference between someone who knows where the information is and someone who has the tools to go get it and put it to work for you.
An agent doesn't just answer questions; it uses Skills to perform tasks in a specific sequence to reach a goal. For beginners and product people, this is the "secret sauce." You aren't just getting text back; you are triggering a workflow.
The New Standard: Skills as Markdown Files
A Skill is a Markdown file (.md) that acts as a manual for the AI.
In late 2025, the way we interact with AI changed. Anthropic introduced a standardized way for agents to handle tasks, and by December 2025, this was promoted to an open standard. Now, all major models (from Claude to GPT) can speak the same "Skills" language.
How the Agent Uses a Skill
Think of your project repository as an office. Inside that office, there is a hidden folder (like .codex or .claude). This is where the Agent's "training manuals" live.
When you give an instruction to an agent like Claude Code or Codex, the workflow looks like this:
- The Trigger: You ask, "Build a new checkout page."
- The Lookup: The Agent checks the
.codex/skillsfolder. - The Loading: It finds a file named
spec-generator.md, reads it, and loads those specific instructions into its "brain" (context) before it types a single word.
The Skills Folder Structure for codex:
my-cool-app/
├── src/ # Your actual code
├── package.json
├── .codex/ # The Agent's "Brain" folder
│ └── skills/ # The Skills Library
│ ..├── spec-generator/
│ . │ ├── SKILL.md # The "Product Manager" instructions
│ . │ └── templates/
│ . │ └── prd_template.md
│ ..├── feature-planner/
│ . │ ├── SKILL.md # The "Lead Dev" instructions
│ . │ └── style-guide.md
└── .gitignore
What a Skill File Looks Like
Because these are just Markdown files, anyone on the product team can read or edit them. Here is a simplified example of what a Spec Generator Skill might look like:
# Skill: Spec Generator
## Role
You are a Senior Product Manager specializing in user-centric web design.
## Instructions
1. When a user asks for a new feature, do not write code immediately.
2. Interview the user to define the "Acceptance Criteria."
3. Identify potential edge cases (e.g., "What if the user is offline?").
4. Accessibility requirements (basic vs extended requirements).
Why This is a Game Changer for Teams
Using this flow, the AI agent can build project features more autonomously and predictably.
By adding just these two ingredients - Spec Generation and Implementation Planning into your existing AI workflow, you shift from "hoping the code works" to a system where the AI agent autonomously and predictably builds the project features for you.
- The Spec (The "What"): Acts as the guardrail. It prevents the AI from "hallucinating" unnecessary features or straying from your business goals.
- The Plan (The "How"): Acts as the blueprint. It ensures the AI understands your specific architecture and security rules before it starts typing.
The result? You aren't just getting code faster. You're automating feature development process and making it more predictable and deterministic.
The Agentic Workflow: Three Steps to Production
Instead of jumping straight into the code, professional AI agents follow a structured path that mirrors how we humans build software:
- Spec Generation: Defining the "What."
- Implementation Planning: Defining the "How."
- Execution: Writing the actual code.
Skill 1: Spec Generation (The "Architect" Skill)
To start enhancing your current AI agent workflow and making it more predictable and produce better results, we are starting off with specs.
Most AI-driven software projects fail quickly because of "vague requirements." If you tell an AI "Build me a login page," you'll get something generic.
An agent with a Spec Generation Skill will interview you. It acts as a Product Manager. It will ask:
- What happens if the user forgets their password?
- Are we using OAuth (Google/GitHub) or email?
- What are the brand colors?
The Result: Before a single line of code is written, the agent produces a Markdown document that outlines every feature. This ensures the AI (and you) are building the right thing.
Skill 2: Implementation Planning (The "Lead Dev" Skill)
Let's continue improving predictability and agent output results by adding another skill - Implementation Planning.
Once the spec is ready, the agent uses its Planning Skill. This is where the "Agent" starts to shine over a basic chatbot.
The agent looks at your existing codebase and says:
"To add this feature, I need to modify
user.controller.ts, create a new migration for the database, and update the frontend API folder."
By creating a Step-by-Step Plan, the agent avoids the "hallucinations" that happen when AI tries to do too much at once. It breaks a big problem into tiny, bite-sized tasks.
Cool thing about this is that you can ask your AI agent to implement a few of the items on the list, then pause to ensure the result is what you need and then ask it implement the rest of it.
Skill 3: Feature Implementation (The "Builder" Skill)
Finally, we reach the implementation. Because the agent has a spec and a plan, the code it writes is context-aware.
Modern agents use a skill called Tools. These allow the agent to:
- Read files to understand your coding style.
- Write files directly into your VS Code editor.
- Run tests to see if the code actually works.
- Fix errors automatically if a test fails.
Why Predictable AI-agent Workflows Matter for Beginners and Beyond
If you are a non-technical founder or a junior dev, the "Agent + Skills" approach is your superpower.
- For Beginners: You learn how to think like an architect. You see the plan before the code, which helps you understand the "why" behind the logic.
- For Product People: You can prototype features in hours instead of weeks. You can generate technical specs that are actually useful for your dev team.
- For Seasoned Developers: It helps you overcome the blank page problem quickly. You can start the implementation with an AI agent and finish it yourself as you see fit.
Final Note: Skills vs. MCP and Why You Need Both
There's a growing misunderstanding on X that skills will replace MCP, but that's far from true.
You might have heard of MCP (Model Context Protocol), another breakthrough from Anthropic. If Skills are the "instructions," MCP is the "infrastructure."
It is important to understand that Skills are not here to replace MCP. They work together.
The Technical Difference
- MCP (The API): Think of MCP like a Restful API. It is a bridge that allows the LLM to reach out and touch "remote" tools. It says, "I have permission to fetch a record from the database" or "I can search for people named Andrii in the CRM."
- Skills (The Local Script): Think of Skills like local scripts or a "playbook." They are internal instructions that tell the agent how and when to use those tools to achieve a specific workflow.
Why the Distinction Matters
You use MCP to give your AI access to your private data (like your Jira tickets or your production database). You use Skills to define your team's unique way of working with that data.
By keeping them separate, the industry has created a modular system: you can swap your database (MCP) without having to rewrite your Product Management "Playbook" (Skills).
The Bottom Line
To wrap up, adding these two ingredients - Spec Generation and Implementation Planning into your workflow allows the AI agent to autonomously and predictably build project features for you. Here is a summary of the three essential skills that make this possible:
- Spec Generation (The "What"): The agent acts as a Project Manager/Tech Lead to define requirements and edge cases. This ensures the AI avoids building unnecessary features and stays focused on your actual goals.
- Implementation Planning (The "How"): The agent acts as a Lead Dev to map out exactly which files to change. This guarantees the output follows your company's unique coding style and security protocols.
- Implementation (The "Action"): The agent executes the plan and writes the code. Because it has the Spec and the Plan in its "brain," the final output is high-quality and fits perfectly into your codebase the first time.
If you liked this article, consider checking my previous AI-related article:
Inside the AI Harness: Engineering Techniques for Predictable and Deterministic AI
Until next time - happy coding! 🧑💻