Agent Design: Instruction-First Optimization in Agentic Automation

Agent Design: Instruction-First Optimization in Agentic Automation

The portfolio reWireAdaptive, in association with the @reWirebyAutomation channel, presents an article on Agent Design. This article, titled "Agent Design: Instruction-First Optimization in Agentic Automation", aims to explore and adopt Instructions in the AI-Enabled Automation Development / Agentic Automation Pattern.

Introduction

As AI becomes deeply integrated into modern development environments, agentic automation is no longer experimental. Most AI tools today operate inside IDEs, observe codebases, and participate continuously in development workflows. However, despite this advancement, inconsistent outputs, unreliable suggestions, and misaligned solutions remain common challenges.

The root cause is often not the AI model itself, but how the agent is instructed.

This article focuses on Instructions as a foundational element of agent design, exploring what instructions are, why they matter, how they should be applied, and the consequences of neglecting them in AI-enabled and agentic automation development.

What are the Instructions in Agent Design?

In the context of AI-enabled development, instructions are explicit constraints and expectations that guide how an AI agent reasons, responds, and generates outputs.

Instructions define:

• The scope of the problem

• The frameworks and standards to follow

• The level of detail expected

• The boundaries the agent must not cross

Unlike traditional automation, where logic is deterministic, AI agents are non-deterministic systems. The same prompt can lead to different outputs unless instructions anchor the reasoning path. Instructions act as the stabilizing layer that converts AI capability into usable engineering outcomes.

Why Instructions Are Critical in AI-Enabled Development

AI systems do not inherently understand project intent, architectural decisions, or organizational standards. Without instructions, the agent attempts to infer context—often incorrectly.

In agentic automation:

• Instructions reduce ambiguity

• Instructions control variability

• Instructions improve alignment with specifications

More importantly, instructions are the only reliable way to guide non-deterministic systems toward consistent accuracy. Strong instructions do not restrict innovation; they enable predictability.

This is especially critical when working with open-source automation stacks, where patterns, conventions, and integrations vary widely.

How Instructions Enable Agentic Pattern Development

Agentic patterns are not created by AI alone; they emerge from repeated, structured interactions between engineers and AI agents.

Instructions enable this by:

• Establishing repeatable reasoning paths

• Reinforcing preferred solution patterns

• Preventing architectural drift across iterations

When instructions are applied consistently, the agent begins to recognize and reinforce patterns such as:

• API validation strategies

• Automation framework structure

• Error-handling approaches

• Reusable utility abstractions

This is how agentic pattern development becomes intentional rather than accidental.

How to Apply Instructions Effectively

Effective instructions are:

• Explicit, not assumed

• Contextual, not generic

• Iterative, not one-time

They should be applied:

• At the start of a session, to establish boundaries

• During refinement to correct direction

• After failures to reinforce expectations

Instructions work best when treated as design artifacts rather than casual prompts. They evolve as understanding deepens and as the agent learns from corrected outcomes.

What Happens When Instructions Are Weak or Missing

The absence of clear instructions leads to:

• Inconsistent automation logic

• Over-engineering or under-engineering solutions

• Increased debugging cycles

• Reduced trust in AI outputs

In practice, this often results in teams abandoning AI assistance altogether—not because AI failed, but because agent design was neglected.

Without instructions, AI becomes reactive instead of collaborative.

Benefits of Instruction-Driven Agent Design

When instructions are applied deliberately:

• Solution accuracy improves

• Agent behavior becomes predictable

• Development velocity increases without sacrificing quality

• Agentic patterns stabilize across iterations

• AI becomes a reliable contributor, not a distraction

The result is optimization through design, not trial-and-error usage.

Repository Level Instructions:

.github/copilot-instructions.md

Example: Playwright (TypeScript) instructions

# Copilot Instructions — Playwright (TypeScript)

You are contributing to a Playwright test automation project.

## Tech & standards

- Language: TypeScript

- Runner: @playwright/test

- Style: async/await, strict typing

- Locators: prefer getByRole/getByLabel; avoid brittle CSS/XPath unless necessary.

## Code patterns (must follow)

- Use Page Object Model where it already exists; do not create new patterns randomly.

- Add stable assertions with expect().toBeVisible()/toHaveText()/toHaveURL().

- Use test.step() for readability when adding multi-phase flows.

- Avoid arbitrary timeouts; prefer auto-waits and expect-based waits.

## Output expectation

- Provide complete runnable test blocks.

- Include minimal comments that explain intent, not obvious syntax.

Example: IntelliJ Custom Instructions (RestAssured Automation)

# Copilot Instructions — RestAssured (Java)

You are contributing to a REST API test automation project.

## Tech & standards

- Language: Java

- Build: Maven

- Test runner: TestNG

- REST: RestAssured

- Assertions: TestNG / Hamcrest

- JSON: Jackson ObjectMapper

- Logging: slf4j

## Code patterns (must follow)

- Use a RequestSpecification builder pattern; avoid duplicating base URI and headers.

- Keep tests readable: arrange/act/assert.

- Use POJOs for request/response when possible; avoid raw string JSON unless necessary.

- Centralize auth header creation; do not hardcode tokens.

- Prefer explicit assertions on status code + key fields + schema-like checks (when available).

## Quality rules

- No flakiness: add deterministic waits only when unavoidable.

- Add meaningful error messages in assertions.

- Do not introduce new dependencies unless explicitly asked.

## Output expectation

- When generating tests, include: endpoint, purpose, preconditions, request, and validation.

- If you’re unsure, ask a single clarifying question OR propose the safest assumption.

“Benefits of doing it” vs “benefits of not doing it”

When you DO provide instructions

• Copilot aligns to your framework patterns faster (less refactoring).

• Outputs become repeatable even though the AI is non-deterministic.

• You reduce “style drift” across files and contributors.

When you DON’T

• You’ll see inconsistent structures (different assertion styles, locator strategies, and auth handling).

• Copilot may invent patterns (new base classes, new folder conventions, random utilities).

• Debugging time increases because the output is “valid-looking” but misaligned.

Stay tuned for the next article from rewireAdaptive portfolio

This is @reWireByAutomation, (Kiran Edupuganti) Signing Off!

With this, @reWireByAutomation has published a “Agent Design: Instruction-First Optimization in Agentic Automation."

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