The term has been showing up for months in technical conversations, job postings and product presentations. And as usually happens with concepts that become popular fast, there’s a lot of noise around what Agentic Engineering actually is and how it differs from what development teams were already doing with AI.
This article clarifies it from a real implementation perspective: what it is, where it comes from, what changes in the team when you adopt it, and what needs to be in place before doing so makes sense.
From code assistant to agent: how we got here
For years, AI in software development worked as an advanced autocomplete assistant. The model suggested a line or a block, the developer reviewed it, accepted or corrected it, and the work remained essentially human. AI sped things up, but it didn’t decide.
That balance changed when language models crossed a quality threshold beyond which their outputs were mostly correct without manual correction. From that point, the work pattern began to change naturally: the developer stopped reviewing every line because most were already good. They began to delegate more, to direct rather than write. The real leap wasn’t the improvement in code generation itself: it was the move from single-exchange assistance to multi-step workflows where the agent can execute, validate and correct without intervention at every step. That’s what opened the door to Agentic Engineering.
What Agentic Engineering is
Agentic Engineering is the professional engineering discipline in which AI agents plan, write, test and deploy code under structured human supervision. The term was coined by Andrej Karpathy (co-founder of OpenAI, former director of AI at Tesla) in early 2026 as an evolution of Vibe Coding: not the casual use of AI to generate quick code, but a methodology built around autonomous agents with the human in the role of architect and validator.
The most precise definition Karpathy himself has given is this: an agent orchestration system writes the code, and the human developer supervises and validates the result. As the agent progresses through the subtasks, the human stays in the loop. They don’t disappear: they change function. They stop being the one who types each implementation and become the one who defines what the system has to do, what criteria it has to meet and when the result is correct.
What makes Agentic Engineering different from any earlier form of AI assistance is the level of autonomy. The agent receives a goal, breaks it down into tasks, executes each one, validates the results and adjusts the plan if something fails, without the human having to step in at every point. The developer’s high-value work shifts from implementation to design, specification and technical judgment.
Agentic Engineering vs. Vibe Coding: the distinction that matters
Before Agentic Engineering, Karpathy coined Vibe Coding. And the difference between the two terms isn’t one of degree: it’s one of purpose and of consequences in production.
Vibe Coding describes the flow where the developer writes a prompt, accepts what the model generates and checks whether it works. If it doesn’t work, they prompt again. The result can be surprisingly good for a prototype, a personal script or a hackathon. The problem appears when that code tries to be maintained, scaled or pass a security review. Karpathy sums it up directly: Vibe Coding raises the floor for beginners; Agentic Engineering preserves the quality ceiling for professionals.
In Agentic Engineering, the agent executes the implementation, but does so within constraints the human has defined beforehand: a design document, a technical specification, an agreed architecture, a set of acceptance criteria. The developer writes less code line by line, but is fully responsible for what reaches production. That responsibility isn’t delegated to the agent. Speed increases; accountability doesn’t change.
In enterprise environments where systems are critical, that distinction has a direct impact on accumulated technical debt, on the team’s ability to maintain what it builds, and on the security of what gets deployed.
What changes in the team when you adopt Agentic Engineering
Adopting Agentic Engineering isn’t installing a tool. It’s a change in how the team works, in which profiles are needed and in what has to be in place before agents start generating production code.
Roles change, they don't disappear
The developer working with Agentic Engineering doesn’t write less code because they’re less capable. They write less because they spend their time on what has the most impact: defining precise specifications, designing the architecture the agent will implement, writing the tests that validate the result is correct, and reviewing the output with real technical judgment. Engineering teams need to train not only in how to prompt better, but in systems design: how to orchestrate agents, validate their output and integrate review loops into existing CI/CD pipelines.
Testing becomes the central piece
The most practical difference between Agentic Engineering and Vibe Coding is testing. Without a solid test suite, an agent will declare broken code finished with complete confidence. With tests, the agent can iterate until everything passes, and the team has real guarantees about the result. In concrete terms: if the team doesn’t have sufficient test coverage before bringing in agents, the agents will generate code that looks correct but hides errors that will only surface in production.
The specification becomes the most valuable artifact
In an Agentic Engineering flow, the quality of the agent’s output depends directly on the quality of the specification it receives. An agent with a vague brief makes design decisions on its own. Those decisions may be reasonable, but not necessarily aligned with what the business needs. The design document, the detailed specification and the acceptance criteria stop being bureaucracy and become the system’s main quality-control mechanism.
The risk no one mentions
The most repeated argument for Agentic Engineering is speed. Organizations working intensively with AI are achieving significant operational cost reductions and much shorter development cycles. Those numbers are real. But there’s a condition that usually stays out of the headline: the speed the agents generate amplifies good architecture decisions as much as bad ones.
A team with well-defined architecture, solid tests and structured review processes can compress weeks of work into days. A team without those foundations will generate the same volume of code in far less time, but that code will accumulate technical debt at a pace that’s also unprecedented. More autonomy without more architecture isn’t progress.
The question any CTO should ask before adopting Agentic Engineering isn’t which tool to use, but whether the processes are in place to validate what the agents generate before it reaches production.
Agentic Engineering and data: the layer that holds it up
Software agents need context: access to databases, APIs, internal systems, technical documentation. The quality of that context determines the quality of what the agent produces. An agent working with poorly structured data, without governance or scattered across silos, makes implementation decisions based on incomplete information, with all the problems that creates in production.
That’s why Agentic Engineering and data architecture aren’t separate disciplines in enterprise environments. The teams that get the most consistent results with agents are those with a well-ordered data layer: single sources of truth, access governance, documented lineage. Connecting agents properly with agentic AI at the infrastructure level isn’t a secondary technical detail: it’s the condition that determines whether the system works reliably beyond the first prototype. Context engineering (how the information the agents receive is structured and curated) is as decisive as the quality of the model that runs them.
How we work with agentic systems at BertIA
At BertIA, we’ve been working for some time with agentic AI architectures for clients in industrial, logistics and pharmaceutical sectors. What we’ve learned in that process is that the most common point of failure isn’t the technology (the models are mature enough for this) but the absence of the layers that have to exist before agents operate with real autonomy.
The pattern that recurs when something doesn’t work as expected always has the same structure: the agents receive ambiguous instructions, don’t have reliable access to the context they need, or there’s no validation process to detect when the output doesn’t meet the business criteria. The solution isn’t to go back to manual supervision at every step. It’s to design the orchestration layer, data access and acceptance criteria well from the start, before the agent begins working.
In the projects where we’ve seen the most real impact, the agentic architecture rests on three elements that, at BertIA, we work on from the start: a well-governed data layer that serves as a reliable context base, an MLOps system that manages the lifecycle of the models the agents use, and a structured review process that keeps the human team in the loop at the decision points that matter.
Conclusion
Agentic Engineering isn’t the future of software development. It’s the present for teams that already build with AI professionally and in a structured way. The difference between the teams that use it well and those that accumulate technical debt faster than ever is exactly there: whether the foundations (architecture, testing, specification, data) were in place before delegating implementation to the agents.
If you’re evaluating how to bring Agentic Engineering into your development team, or how to build the architecture agents need to work reliably in production, talk to our team to review your specific case.
