Agentic Development: How to Build Software with AI Agent Workflows

The real transformation isn't "chat with AI to help you code." It's agent mode: giving AI autonomous access to your repository, extended context across dozens of files, and systematic workflows for complex engineering tasks.

I'vWhat Is Agentic Development?

Traditional AI coding: Copy/paste between chat and editor. AI suggests, you implement.

Agentic development: AI has tool access to your entire repository—reading files, writing code, running scripts, searching context, managing dependencies.

The shift: From AI as advisor to AI as autonomous engineer working within your systematic structure.

1. Repository-Structured Workflows: Architecture Over Ad-Hoc

The Pattern: Structure your repository so AI agents can navigate, understand, and extend systematically.

What this looks like:

project/
  README.md              # Context: what this is
  docs/                  # Architecture, specs, decisions
  scripts/               # Automation, tooling
  schema/                # Data contracts, validation
  .github/
    copilot-instructions.md  # How to work with this repo

Example - SEO System Build:

Me: "Build a comprehensive SEO system for this blog. Validator script, 
templates for new posts, documentation for best practices, and 
deployment workflows."

Agent: [Reads repo structure, existing content patterns]
      Extended Context: Multi-File Understanding

**The Pattern:** Agent reads across dozens of files to understand system behavior, not just isolated code.

**How agents use extended context:**
1. Semantic search across codebase for relevant patterns
2. Read configuration files, schemas, documentation
3. Check dependencies, imports, cross-references
4. Understand **system architecture**, not just functions

**Example - Blog Post Optimization:**

Me: "Optimize the AI Development series posts for SEO. All 7 parts."

Agent: [Searches for all series posts across /content/]
[Reads existing frontmatter patterns from 50+ posts]
[Checks /docs/ for SEO guidelines]
[Validates against /schema/ if it exists]
[Modifies all 7 posts consistently]
[Generates hub page linking all parts]
[Runs validation to confirm changes]


**What the agent tracked:**
- 7 blog post files (content)
- 50+ Systematic Instructions: Teaching Agents Your Standards

**The Pattern:** Document your coding standards, architecture decisions, and workflow rules. Agents follow them automatically.

**Repository instruction files:**

.github/
copilot-instructions.md # Project philosophy, architecture
instructions/
python-scripts.instructions.md # Python code standards
documentation.instructions.md # How to write docs
git-workflow.instructions.md # Commit conventions


**What gets encoded:**
- Code style (PEP 8, line length, naming conventions)
- Architecture patterns (where files go, how to organize)
- Testing protocols (separate test areas, cleanup requirements)
- Git practices (commit message format, branch strategy)
- Quality checklists (what to verify before completion)

**Example - Python Script Generation:**

Me: "Build a script to aggregate weekly summaries from daily logs."

Agent: [Reads python-scripts.instructions.md]
[Follows PEP 8, uses pathlib not os.path]
[Adds type hints, docstrings with purpose/params/returns]
utonomous Task Execution: From Request to Deployment

The Pattern: Give high-level goal, agent handles the multi-step execution autonomously.

What agents do without hand-holding:

Search for existing patterns before creating new ones
Read relevant documentation and architecture files
Make architectural decisions based on project conventions
Create multiple related files (code + docs + tests)
Run validation scripts to verify correctness
Update cross-references and dependencies
Test changes in isolated environments
Generate deployment checklists

Example - Complete Feature Build:

Me: "Improve blog SEO. Need validation, templates, best practices docs, 
and ensure future posts maintain quality."

Agent: [Searches existing content to understand patterns]
       [Reads blog structure, frontmatter conventions]
       [Designs validator with 10+ SEO checks]
       [Builds Python script with proper error reporting]
       [Creates 3 content templates matching existing style]
       [Generates 60-page best practices doc from research]
       [Writes deployment guide linking everything]
       [Creates quick reference for daily use]
       [Runs validator against all 59 existing posts]
       [Generates audit report with specific issues]
       [Optimizes 7-post series fixing all SEO problems]
      Integration With Development Tools: Full Environment Access

**The Pattern:** Agents don't just write code—they run terminals, manage git, install dependencies, execute tests.

**What agents can do in development environment:**
- Run shell commands (build, test, deploy scripts)
- Execute Python/Node/other runtimes directly
- Install packages via pip, npm, etc.
- Run git commands (status, diff, commit, push)
- Start background processes (servers, watchers)
- Check error logs and terminal output
- Manage virtual environments
- Deploy to staging/production

**Example - Full Deployment Workflow:**

Me: "Deploy to staging first, then production if it looks good."

Agent: [Checks git status for uncommitted changes]
[Runs ./deploy-staging.sh --force --include-assets]
[Waits for deployment to complete]
[Curls staging URLs to verify new content live]
[Reports staging URLs for human verification]
[Waits for approval]

Me: "Production looks good, but check what's changing first."

Agent: [Runs git diff to show all changes]
[Summarizes: 13 new files, 7 optimized posts, 2 new posts]
[Identifies draft post accidentally marked 'publish']
How to Structure Repositories for Agentic Development

The foundation: Good repository structure = effective agent workflows.

Essential patterns:

Single source of truth - One canonical location for each type of information

/daily-log/     # Primary data (WORM - write once, read many)
/data/          # Derived datasets (generated from primary)
/docs/          # Knowledge base (manual, strategic)

What You Still Own (Human Responsibilities)

Agents are powerful but not infallible. You're the architect and quality gate.

Strategic decisions:

✅ What to build (feature prioritization)
✅ Architecture choices (when agent offers options)
✅ Security verification (especially user input, auth, data handling)
✅Real-World Results: What Agentic Development Enables

6 months building this way:

Built in hours, not days:

Complete SEO system (validator, templates, 60pg docs, deployment workflow) - 90 minutes
Content migration pipeline processing 1000+ posts - 3 hours
Multi-file blog post optimization (7 posts + 2 new posts) - 45 minutes
Enterprise AWS CDK infrastructure - 2 days vs 2 weeks traditional

Quality improvements:

Fewer bugs (agents validate as they build)
Consistent code style (instruction files encode standards)
Better documentation (agents generate alongside code)
Systematic architecture (repository structure enforces patterns)

Mental bandwidth freed:

No "remember the pattern" tax
No manual file navigation across dozens of files
No repetitive refactoring across similar code
No context-switching between editor/terminal/docs

But also learned:

Agents still make architectural mistakes (verify decisions)
Security requires human review (don't trust blindly)
Complex debugging sometimes needs human insight
Final quality ga with Agentic Development

Week 1: Structure your repository

Add README.md explaining project purpose and architecture
Organize files into clear folders (docs/, scripts/, etc.)
Create .github/copilot-instructions.md with coding standards
Document existing patterns so agents can follow them

Week 2: Enable agent mode

Use Claude with Sonnet 4.5 in agent mode (not chat mode)
Give simple repository task: "Read the README and summarize the project"
Try file operations: "Create a new script in /scripts/ following existing patterns"
Verify agent follows your repository conventions

Week 3: Multi-file workflows

Task requiring multiple files: "Build a validation system with script + docs + tests"
Let agent search for existing patterns before creating
Review how agent organizes new files
Update instruction files if agent needs guidance

Week 4agentic development: These patterns emerged from 6 months of daily production use across enterprise infrastructure, content systems, and application development. The examples are real workflows, not theoretical demonstrations. Agent capabilities evolve rapidly—this reflects December 2025 state-of-the-art with Claude Sonnet 4.5.

The real transformation: Not "AI helps you code faster." It's systematic repository structure + extended context + autonomous execution = software engineering at a fundamentally different sca

Give high-level goal: "Optimize all blog posts in the X series for SEO"
Let agent figure out the steps (search, read, modify, validate)
Supervise but don't micro-manage each action
Review final result, verify quality

Week 5: Full development cycles

Feature build: "Add RSS feed generation to the blog"
Let agent handle: research, design, implementation, testing, docs, deployment prep
Focus your time on: architectural decisions, security review, final verification
Deploy to production after validation

Key mindset shift: You're not "chatting with AI." You're **architecting systems that AI agents can navigate and extend autonomously
The balance: Agents handle execution autonomy. You handle strategic direction and final verification.


3. **Documentation at every level** - READMEs in each major folder

/scripts/README.md # What scripts exist, what they do
/docs/README.md # Documentation index
/schema/README.md # Schema versioning, validation


4. **Instruction files** - Teach agents your standards

.github/copilot-instructions.md # Project philosophy
.github/instructions/*.md # Domain-specific rules


5. **Metadata for discoverability**

Top of important files

"""
Purpose: Validates blog post frontmatter against SEO requirements
Usage: python scripts/seo-validator.py content/published/**/*.md
Dependencies: pyyaml, jsonschema
"""


**Result:** Agents can navigate, understand intent, extend systematically
1. Simple script - linear processing (easy, but slow)
2. Concurrent futures - parallel processing (faster, more complex)
3. Pipeline with generators - memory efficient (best for large scale)

For 50 files, I'd recommend #2: [code example with ThreadPoolExecutor]"

Me: "Show me error handling for this pattern"

Claude: [Demonstrates exception handling in concurrent context]

Result: Better architecture decisions, learned new patterns.

5. Learning New Technologies: Accelerated Skill Acquisition

The Pattern: Ask for explanations, examples, and best practices.

How I use it:

Ask for concept explanation
Request working example
Modify example for my use case
Ask follow-up questions
Build understanding through iteration

Example:

Me: "Explain Schema.org structured data for blog posts. Show example for Astro."

Claude: [Explains Article schema, shows JSON-LD example]

Me: "How do I make this dynamic in an Astro component?"

Claude: [Shows component with props, dynamic schema generation]

Me: "What about breadcrumbs and author schema?"

Claude: [Expands example with nested schemas]

Result: Learned Schema.org markup in 20 minutes, implemented in production in an hour.

Technologies I've learned this way: Schema.org, advanced Python patterns, Astro framework details, AWS CDK architecture.

The Meta-Pattern: Context is King

Across all five workflows, the most important skill is managing context.

Bad prompt:

"Fix this error" [paste traceback]

Good prompt:

"I'm building a blog post validator. It should check frontmatter against a schema. Getting this error on multiline YAML strings: [traceback]. Here's the relevant code: [snippet]. Expected: parse multiline correctly. Actual: crashes on >- syntax."

The difference: Claude needs to know what you're building, what you expected, and what actually happened.

Common Mistakes to Avoid

❌ Blindly copying code - Read it, understand it, verify it
❌ Not testing - Claude makes mistakes; you're the quality gate
❌ Over-relying - Your judgment matters; Claude is a tool
❌ Skipping documentation - Still need to understand the fundamentals
❌ Ignoring security - Always audit anything touching user input or sensitive data

My Real-World Results

6 months of daily Claude usage:

Built complete AI Development Revolution series
Migrated enterprise infrastructure to AWS CDK
Created automated content processing pipelines
Shipped production applications
10x velocity increase on new features
Fewer bugs (code review workflow catches issues early)

But also:

Still had to debug AWS permissions manually
Still rewrote architectures when AI suggestions didn't scale
Still maintain final responsibility for code quality

Getting Started

Week 1: Try rapid prototyping on a simple script
Week 2: Use as debugging partner for errors
Week 3: Request code review before merging PRs
Week 4: Tackle a new technology with Claude as teacher

Key mindset shift: You're not "using AI to code." You're coding with AI as a thought partner.