AI and Software Development, Part 5: Systems, Context, and How AI Agents Actually Work

In Part 4, I covered how to build an AI-driven development team through structured role-playing and vigilant orchestration. This methodology elevates you from coder to director.

But to run this team effectively, you need to understand the engine room—the practical systems and underlying mechanics that make this collaboration reliable, repeatable, and independent of any single tool.

This fifth installment pulls back the curtain. We'll cover how to manage the core components of this partnership (prompts and context) as deliberate assets, and we'll demystify what's actually happening when you use tools like Cursor, Claude Code, or GitHub Copilot.

Part 1: Building Your Asset Library – Prompts and Context

The "vibe coder" works in a single chat window. The professional orchestrator works with a library of reusable, version-controlled assets.

1. Prompts as Reusable Templates

Your specialized role prompts (Security Architect, UX Reviewer) shouldn't be typed fresh each time. They are templates. Store them in a knowledge base (like a prompts/ directory in your project or a dedicated tool).

Example prompts/security_review.md:

# Role: Security Architect
## Core Instruction
You are a senior application security engineer. Your task is to review technical designs and code for security vulnerabilities, prioritizing the OWASP Top 10. You are pessimistic and thorough. Do not assume best practices are followed.

## Output Format
1.  List each potential vulnerability.
2.  Provide a severity rating (Critical/High/Medium/Low).
3.  Reference the specific code/design line.
4.  Suggest a concrete mitigation.
5.  If no issues are found, state: "No major vulnerabilities found, but consider [suggest one hardening step]."

## Start of Task

This turns a complex prompt into a single line in your chat: "Please adopt the Security Architect role (see attached prompt) and review this API route."

2. Context as Deliberate Documentation

Relying on an AI tool's "memory" of a long conversation is a losing strategy. Context fades, gets diluted, or is deemed irrelevant.

The professional approach is just-in-time context injection. Maintain living documents and feed the exact relevant excerpts when needed.

Project Bible (project_context.md): Core tech stack, key architecture decisions (e.g., "We use REST, not GraphQL"), and non-negotiable standards.
Module/Feature Specs (feature_auth_spec.md): Detailed requirements, user stories, and UI/UX constraints for a specific sprint.
Architecture Decision Records (ADRs): The why behind key technical choices.

How you use it: At the start of a new chat or a new sprint phase, you don't say "remember what we're doing." You say: "Here is our Project Bible and the Feature Spec for the authentication module. Based on these, act as the Principal Developer and draft an API contract."

This achieves tool agnosticism. Your process is defined by your asset library, not by a specific AI tool's features. You can switch from Claude to ChatGPT to a local model, and your core system remains intact.

Part 2: Demystifying the AI Coding Agent

When you ask an AI coding tool to "implement this function," it's not just a single prompt and response. Internally, it's often running a multi-step agentic workflow. Here’s a simplified view of what happens:

Planning & Decomposition: The agent first analyzes your request. "Implement user login" might be decomposed into: a) check database schema, b) generate route handler, c) generate password hashing utility, d) generate token creation logic.
Iterative Prompt Chaining: It doesn't write all the code in one go. It may write a function, then prompt itself: "Does this function handle edge case X? If not, revise." This is an internal "chain-of-thought" or "chain-of-revision" loop that mimics a developer's iterative thinking.
Tool Use – The Key to Action: The LLM's core capability is generating text. To affect the world (edit a file, run a test, read a directory), it must use tools. This is where protocols like the Model Context Protocol (MCP) come in.
- Built-in Tools: File read/write, terminal commands, code search.
- MCP Servers: These are external servers that provide specific context or capabilities (e.g., a server that fetches current documentation, queries your database schema, or enforces custom linting rules). The AI agent can call these tools to gather information before acting.
Validation & Loop Closure: After making changes, a sophisticated agent might trigger a tool to run tests or linters, read the results, and then decide if another revision is needed.

Why this matters to you: Understanding this flow explains the latency (it's thinking step-by-step), the importance of tool access (without file editing, it's just a chatbot), and the source of errors (a flawed plan in step 1 derails everything). It also shows why your upfront context (Project Bible) is so critical—it seeds that initial planning phase with the right constraints.

Part 3: Navigating the Black Box – System Prompts and Memory

You are collaborating with two entities: the base LLM (e.g., GPT-4, Claude 3) and the AI coding tool's wrapper (Cursor, etc.). This wrapper has immense influence through its system prompt and its memory management.

The Invisible System Prompt: Tools come pre-loaded with a system prompt like: "You are an expert software engineer. Be concise, generate correct code, help the user." You can't usually edit this directly, which is why an AI tool might default to being overly eager and agreeable—it's literally been told to be "helpful" above all. Your user prompts and role assignments are your primary lever to override this default persona.
"Memory" is an Illusion: The tool's "memory" is just the recent conversation history being re-fed as context. As we know, this is fragile. Your asset library is your true, persistent memory. The tool's chat history is merely the volatile working session.

Synthesis: The Orchestrator's Technical Stack

Putting it all together, the professional AI-augmented developer's stack looks like this:

Foundational Layer (Yours): A repository of prompt templates and context documents. This is your source of truth.
Execution Layer (The AI Tool): The agentic workflow that uses LLMs, planning, and tools to execute tasks.
Control Layer (You, the Orchestrator): You initiate work by selecting assets from Layer 1 and injecting them into Layer 2. You validate outputs, guide iterations, and update your foundational assets based on what you learn.

Conclusion: Mastering the Machine

True mastery of AI in development isn't about finding the perfect prompt. It's about building systems.

It's about recognizing that prompts and context are capital to be invested, that AI agents are complex workflows to be understood, and that your value lies in architecting and governing this entire process. You are not just directing an AI; you are designing and operating the system in which it works.

This approach future-proofs your skills. The underlying LLMs will get faster, agents will get smarter, and new tools will emerge. But the principles of asset management, context control, and systemic oversight will remain the constants of professional, augmented software engineering.

This concludes our five-part series on AI and Software Development. We've journeyed from foundational use to advanced orchestration. We hope it empowers you to build better software, with AI as a powerful partner under your expert direction.

Missed a part? Read the full series: