Today's AI agents present unique challenges that set them apart from conventional deterministic software. While traditional software relies on predictable inputs and outputs, agents must interpret open-ended user commands and navigate unpredictable scenarios. This unpredictability gives agents exceptional power, but also creates new risks for production environments.
Leading organizations such as Clay, Vanta, LinkedIn, and Cloudflare have discovered that standard software engineering approaches are no longer sufficient. To address these challenges, they are pioneering a discipline known as agent engineering.
Agent Engineering Explained
Agent engineering is an iterative process designed to increase the reliability of non-deterministic LLM systems in real-world contexts. Rather than seeking perfection before deployment, teams focus on a continuous improvement cycle: build, test, ship, observe, refine, and repeat. Each iteration reveals new insights, enabling teams to address unexpected behaviors and continually enhance reliability.
This closely resembles the standard development cycle with the difference being we are constantly refining input requirements, guardrails and more to adopt to the non-deterministic nature of LLMs and avoiding the "perfect before launch" mindset.
Core Skillsets Required
- Product thinking: Defines the agent's purpose, crafts prompts to shape behavior, and sets up rigorous evaluations.
- Engineering: Develops the technical foundation, including agent tools, intuitive interfaces, and resilient runtimes.
- Data science: Monitors performance, analyzes errors, and drives improvements using data-driven feedback.
How Agent Engineering Emerges Across Teams
Agent engineering isn't just a new job title, it's a cross-disciplinary responsibility. As agents become capable of complex reasoning and adaptation, teams across engineering, product, and data science must expand their skillsets to meet new demands.
- Software and ML engineers: Write prompts, build agent tools, and analyze agent decision paths.
- Platform engineers: Ensure robust infrastructure for agent operations and human-in-the-loop interventions.
- Product managers: Define agent scope and refine behavior using user feedback.
- Data scientists: Track agent reliability and identify improvement opportunities.
Collaboration and rapid iteration are crucial. Teams observe agents in real-world use, identify issues, and work together to refine both agent logic and supporting systems.
Why Agent Engineering Is Essential Now
Two major shifts drive the urgency for agent engineering:
- LLMs now enable agents to perform multi-step, complex workflows. Agents are handling entire jobs, delivering real value in areas like research, outreach, and recruitment.
- This capability brings unpredictability. Agents reason, adapt, and use tools in dynamic contexts, making their actions difficult to anticipate or debug with traditional methods.
Key Challenges
- Every input is an edge case: Users interact with agents using natural language, demanding adaptability to a wide range of scenarios.
- Debugging is different: Much of the agent's logic lives within the model, requiring careful tracing of decisions, where small changes can have major effects.
- Success is not binary: High uptime doesn't guarantee that agents act in line with intended logic or make the best decisions.
Putting Agent Engineering Into Practice
Agent engineering replaces the "perfect before launch" mentality with a cycle of continuous deployment and learning. The typical workflow looks like this:
- Build the foundation: Architect the agent to balance deterministic and LLM-driven decisions.
- Test plausible scenarios: Simulate interactions to catch obvious faults, accepting that not every edge case can be predicted.
- Ship to production: Deploy early to gather authentic usage data and surface unforeseen behaviors.
- Observe and analyze: Trace agent interactions, review decisions, and assess performance against meaningful benchmarks.
- Refine continuously: Update prompts and tools based on observed failures, incorporating new scenarios into testing.
- Repeat: Each cycle increases agent reliability and effectiveness.
Building Trustworthy, Production-Grade AI Agents
The most effective teams view production as the primary learning environment. Agent engineering is emerging as a vital discipline, modern AI agents offer incredible value, but only when their unpredictable behaviors are systematically refined. For organizations aiming to build trustworthy, production-ready AI agents, embracing agent engineering is no longer optional, it's essential.
Let's Build Your Reliable AI Agents Together
Thanks for reading! Agent engineering is reshaping how we think about building reliable AI systems, and I find it energizing because it aligns perfectly with how I approach every client project. With over two decades of experience helping startups, universities, and tech giants like Samsung and Google ship real solutions, I have seen firsthand how iteration and collaboration turn ambitious ideas into production-ready systems.
If you are exploring AI agents for your business and want a partner who understands both the technical architecture and the strategic product thinking required, I would love to hear from you. Whether you need help building intelligent automation workflows or integrating LLM-powered agents into your existing systems, my software development and automation expertise can help you move from prototype to production with confidence. Let's schedule a free consultation and discuss how to bring your agent vision to life.
Agent Engineering: The New Discipline Shaping Reliable AI Agents