As AI development matures, new language is emerging to reflect how engineers are working with new technology and thinking about their systems. “Agentic engineering” is the newest term proposed by OpenAI co-founder Andrej Karpathy to describe the next phase of AI-supported software development.
“Many people have tried to come up with a better name for this to differentiate it from vibe coding, personally my current favorite "agentic engineering": "agentic" because the new default is that you are not writing the code directly 99% of the time, you are orchestrating agents who do and acting as oversight. "engineering" to emphasize that there is an art & science and expertise to it. It's something you can learn and become better at, with its own depth of a different kind.”
Andrej Karpathy
Co-Founder, OpenAI
Terms like vibe coding went semi-viral as AI coding emerged as a brand-new technology. Now, agentic engineering looks forward to what comes next: a more disciplined, accountable, and business-ready approach to building software with autonomous systems. Organizations that understand how AI can be incorporated strategically into their engineering processes will move faster and more safely than they could before.
Here are some of the terms that are emerging or have become freshly relevant to AI engineering today.
Agentic Engineering
A software development discipline in which humans design goals, constraints, and quality standards, while autonomous AI agents execute much of the implementation work under supervision. Unlike traditional coding or casual AI assistance, agentic engineering emphasizes orchestration, verification, and governance. Engineers act as system designers and overseers rather than primary code authors, ensuring outputs meet technical, security, and business requirements.
Why it matters: This approach to AI-assisted coding can dramatically increase development speed without sacrificing reliability if it’s done with proper oversight.
Vibe Coding
An informal style of AI-assisted programming where developers rely heavily on large language models to generate code based on natural-language prompts, often with minimal review or architectural planning. Vibe coding prioritizes speed, creativity, and exploration, making it well-suited for prototypes and experiments but risky for production systems due to limited oversight and verification.
Why it matters: Vibe coding is useful for experimentation and demos, but risky for core business systems because errors and security issues can slip through unnoticed.
What is vibe coding?
Read about AI developmentAI Agent
A software entity powered by an AI model that can take actions autonomously to achieve a goal. In software development contexts, an AI agent may generate code, run tests, refactor files, or interact with external tools. Unlike a simple prompt-response model, an agent can observe outcomes, reason about next steps, and iterate toward completion.
Why it matters: Agents move AI from being a “suggestion tool” to an active contributor, which increases both productivity and risk if unmanaged.
Agentic Orchestration
The practice of coordinating multiple AI agents within a defined workflow. Agentic orchestration determines which agents perform which tasks, what permissions they have, how they communicate, and when humans must intervene. This layer is central to agentic engineering, as it enforces structure, sequencing, and accountability across autonomous systems.
Why it matters: Orchestration turns AI from a loose experiment into a controlled business process. Many businesses are already working with multiple disconnected AI agents designed for specific tasks. Orchestrating these agents helps businesses get more value from their AI systems by fluidly coordinating them within larger business processes.
Human-in-the-Loop
A design principle in which human oversight is deliberately embedded into automated workflows. In agentic engineering, humans review outputs, approve changes, define constraints, and intervene when agents encounter ambiguity or failure. This approach balances automation with accountability and reduces the risks of unchecked autonomy.
Why it matters: This is how organizations stay accountable and compliant while still benefiting from automation and cutting-edge technologies.
Autonomous Coding
The ability of AI systems to write, modify, and validate code without direct human input at each step. Autonomous coding does not mean unsupervised coding; in agentic engineering, autonomy operates within predefined guardrails such as tests, permissions, and review checkpoints.
Why it matters: Autonomous coding is an important tool in the modern engineering toolbox and can be used to speed up delivery while under close human oversight.
Multi-Agent System
A system composed of multiple AI agents, each with specialized roles or responsibilities. In software development, one agent might focus on implementation, another on testing, and another on security or documentation. Multi-agent systems allow complex tasks to be decomposed and handled in parallel while maintaining separation of concerns.
Goal-Driven Development
A development approach where desired outcomes are defined at a high level (for example, “build a feature that meets these acceptance criteria”) rather than prescribing exact implementation steps. Agentic systems use these goals to plan and execute tasks, adjusting behavior based on feedback from tests and validations.
Verification Loop
A continuous feedback cycle in which AI-generated outputs are tested, evaluated, and either accepted or revised. Verification loops may include unit tests, integration tests, performance benchmarks, security scans, or business-rule checks. These loops are essential to making agentic engineering reliable and production-ready.
Why it matters: Without verification, AI output can look correct while being fundamentally wrong.
Guardrails
Constraints that are placed on AI agents to limit risk and ensure compliance. Guardrails can include permission boundaries, restricted file access, enforced coding standards, mandatory tests, or approval requirements. In agentic engineering, guardrails are what distinguish structured automation from uncontrolled experimentation.
Why it matters: Guardrails reduce legal, security, and operational risk.
Oversight Layer
The combination of processes, tools, and human roles that are responsible for monitoring and controlling agent behavior. The oversight layer ensures that AI agents operate within defined boundaries and that their outputs align with organizational goals, quality standards, and regulatory requirements.
Prompt-Driven Development
A development pattern where natural-language prompts are the primary interface for instructing AI systems. While prompt-driven development underpins both vibe coding and agentic engineering, the latter augments prompts with structure, memory, verification, and orchestration to support complex, long-lived systems.
Production-Grade AI Development
The application of AI tools and agents in environments where reliability, security, scalability, and maintainability are critical. Production-grade development requires rigorous testing, documentation, monitoring, and governance; Areas where agentic engineering provides clear advantages over more informal AI coding approaches.
Why it matters: Most AI experiments fail to reach production because they lack governance, monitoring, or accountability.
Technical Governance
The policies and mechanisms used to manage how software is built, changed, and deployed. In agentic engineering, technical governance extends to AI agents themselves, defining what they are allowed to do, how their actions are audited, and who is responsible for outcomes.
Why it matters: Governance ensures AI adoption aligns with legal, security, and business standards.
Cognitive Load Shift
The transfer of mental effort from low-level implementation details (such as syntax and boilerplate) to higher-level concerns like system design, correctness, and intent. Agentic engineering deliberately shifts cognitive load away from typing code and toward reasoning about outcomes and constraints.
Why it matters: With AI shifting the mental load off repetitive, basic tasks, engineering teams can focus on bigger-picture thinking. This will change the structure of engineering teams over time and require looking for different skill sets when hiring.
Autonomy Gradient
A spectrum describing how much decision-making power an AI system has, ranging from simple suggestion (low autonomy) to full task execution (high autonomy). Agentic engineering deliberately places systems at specific points along this gradient depending on risk tolerance and task complexity.
Agent Failure Mode
A predictable way in which an AI agent can produce incorrect, incomplete, or harmful results, such as misinterpreting requirements, generating insecure code, or looping endlessly. Understanding and mitigating agent failure modes is a core responsibility in agentic engineering.
