The Future of Engineering with Generative AI: A Vision for 2025

This blog post dives into a transformative vision for software engineering in 2025, where generative AI, AI agents, and AI assistants become indispensable tools for maximizing productivity and building at unprecedented rates. This is not just about code generation; it’s about fundamentally changing how engineers ingest data, synthesize information, and create valuable outputs.

Estimated Completion Time: 15-20 minutes Prerequisites: Basic understanding of software engineering concepts, familiarity with AI tools (beneficial but not required)

Introduction: The AI Revolution in Engineering

The year 2024 has seen several crucial advancements in AI tooling that are now paving the way for a new era in engineering. This post explores how these advancements – particularly in language models, structured outputs, reasoning models, and real-time APIs – are reshaping the engineering landscape. You’ll learn about:

The key AI tools that are driving this change
The new workflow and mindset required for engineers
A practical example of how to apply these technologies

What You Will Learn

By the end of this post, you will understand:

How generative AI tools are transforming software engineering
The roles of prompts, AI agents, and AI assistants in this new paradigm
Practical strategies to integrate AI into your workflow to boost productivity

The Power of AI Tooling: Key Advancements

Several groundbreaking advancements in AI tooling have made this future vision possible. These tools are not just incremental improvements; they are fundamental shifts that enable a completely new approach to software development. Let’s break down the core elements:

Large Language Models (LLMs): At the heart of this revolution lies the powerful generative AI engines, known as Large Language Models (LLMs). These are the text and vision models that drive the underlying capabilities of most AI tools. Without the LLM, nothing else is possible.
Prompts: The prompt has emerged as the fundamental unit of knowledge work. If you master the prompt, you will master knowledge work. The ability to craft effective prompts is essential for leveraging the power of these models.
AI Agents: These are prompts combined with logic and data to solve a specific problem. If you’ve been using AI coding tools, you’ve already worked with AI Agents. They are specialized tools wrapping logic, code, and UI on top of LLMs.
AI Assistants: This is the orchestration layer that enables engineers to manage and control multiple AI agents across various use cases, allowing engineers to work on multiple projects in parallel. This is where you see a true boost in engineering output.
Real-time API: This allows AI tools to operate in real-time, enabling seamless integration and workflows for real-time processing and interaction, critical for AI assistants.
Structured Outputs: The ability of AI models to produce structured outputs such as JSON or CSV is essential for building automated pipelines and processing information effectively.
Reasoning Models: These are models that enable AI to understand and process information in a logical way, allowing for the creation of sophisticated AI agents and assistants.

The Engineer’s New Role: Orchestration and Management

The role of an engineer is no longer just about writing code. In this new era, engineers become orchestrators and managers of compute. We’re shifting from manually writing every line of code to designing systems and workflows that leverage AI tools.

Traditional Engineering Workflow

Ingest Data: Gathering information from databases, codebases, documentation, blogs, trends, news, research tools, and other sources.
Synthesize Output: Transform the gathered information into code, research, media, content, and products.

The Enhanced Workflow with AI

Ingestion: Leveraging AI tools to automate and accelerate data collection and information gathering.
Synthesis: Using AI agents and assistants to generate code, documentation, research, and other artifacts.

The Generative AI Composition Chain: From Prompts to Agentic Systems

Here’s a breakdown of the different levels of AI integration, forming a composition chain where each level builds on the previous one:

Prompts: The fundamental unit of knowledge work. Crafting the right prompt allows you to harness the power of LLMs.
AI Agents: These combine prompts with logic and data to solve specific problems. This might include AI coding tools that wrap logic, code, and UI around LLMs.
AI Assistants (Orchestration Layer): These allow engineers to orchestrate multiple AI agents across various use cases. They act as a fast interface for your workflows, allowing you to control AI and work on tasks in parallel.
Agentic Systems: Full-on, self-operating software that works on your behalf, where AI prompts you for what it needs to do next, and is no longer just the other way around. This is a longer-term goal of the evolution of AI in software engineering.

Warning: Full agentic systems are not something to expect in the immediate future, they are a significant undertaking. Focus on mastering prompts, AI agents and AI assistants for now.

A Concrete Example: Working with Databases and Ada

Let’s examine how an AI assistant can supercharge your workflow. Consider a database use case:

Traditional Approach: Manually writing SQL queries, generating charts, and organizing data, a time-consuming process.
AI-Enhanced Approach: Using an AI assistant like “Ada” to:
- Load SQL tables into memory
- Generate markdown documents of table definitions.
- Execute SQL queries to fetch specific product data.
- Generate CSV files for analysis.
- Create bar charts directly from SQL data or CSV data in Python.
- Manage file operations, such as deleting files.

This level of automation dramatically reduces the time and effort required to work with databases. It is a clear illustration of how an orchestration layer such as a personal AI assistant can improve the speed, reliability and productivity of software engineers.

Orchestration Layer Advantage

The AI assistant allows actions across multiple domains, like SQL generation, documentation and visualization.
It goes beyond simple tasks to help you manage projects more effectively.

Your 2025 Plan: Integrating AI into Your Workflow

The core question you should be asking yourself right now is: “How can I use generative AI to help me do my tasks faster, better, or cheaper?” Here’s how to start incorporating AI tools into your routine:

Identify Repetitive Tasks:
- What operations consume the most of your time?
- How often do you perform these tasks?
- Which tasks are critical to solve with speed and efficiency?
Apply the Generative AI Composition Chain:
- One-off tasks: Use a general-purpose tool with a prompt.
- Recurring Tasks (2+ times): Start with reusable prompts or scripts.
- Frequent tasks : Develop specialized AI agents or tools.
- Mission-critical tasks: Invest in building or finding an AI assistant to manage and orchestrate the various AI agents, and the overall workflow.
Common Software Engineering Operations to Automate:
- Database interactions (SQL, ORM, data access)
- Codebase navigation and modification
- Documentation consumption and creation
- Filtering and processing relevant information (blogs, trends, news, research)
- Code generation and review
- Information and research output
- Media and content creation
- Full product development

Productivity Gains: Estimated Impact

The real value comes from moving from prompts to AI agents, and then to orchestration layers. Here’s a rough estimate:

2x Gain: Utilizing tools that give access to language models. This is the first step in AI integration.
5x Gain: Leveraging specialized AI agents for specific problems. This is where you see significant jumps in productivity.
10x Gain: Orchestration with AI assistants over AI agents across multiple use cases, which dramatically increases efficiency and allows for greater control.
100x+ Gain: The potential of fully agentic systems which is where AI operates autonomously on your behalf. Note that this is a long term goal.

Tip: Start with the most frequently used operations in your workflow. As you automate them, move up the composition chain from prompt, agent to assistant and further.

Looking Ahead: Meta Prompting, AI Coding, and Agentic Systems

Before the year ends, there will be more insights into these advanced techniques:

Meta Prompting Concepts: Techniques to refine prompts for better outcomes.
AI Coding Course: A guide on using today’s and tomorrow’s AI coding tools.
Next-Generation AI Tooling: Continued development on AI agents and personal AI assistants, all the way to fully agentic systems.

Conclusion: Embracing the AI-Driven Future

The future of engineering is being reshaped by generative AI. As engineers, we need to embrace these tools, learn how to orchestrate them, and continuously adapt to the new capabilities they offer. By focusing on the critical operations that consume most of our time, and matching them to the right level of AI integration, we can unlock unprecedented levels of productivity. The key takeaway is that the engineer of the future is the manager and commander of compute.

Next Steps

Start experimenting with AI coding tools.
Identify your most repetitive tasks and look for ways to automate them.
Keep learning about new AI advancements and refine your approach to integrating them into your workflows.
Engage with communities to share your journey with AI in software engineering.

Stay focused and keep building the future of software.