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From Vibe Coding to Agentic Engineering: The Next Era of Software Development

February 10, 2026

Andrej Karpathy coined 'vibe coding' -- now a new discipline is emerging: Agentic Engineering. Where AI agents autonomously plan, write, test, and evolve code under structured human oversight. From tools to teammates.

From Vibe Coding to Agentic Engineering: The Next Era of Software Development

In just a few years, AI has taken software development from autocomplete to something far more radical. First came AI-assisted coding, then "vibe coding" -- a term coined by Andrej Karpathy to describe building software by talking to an LLM and "forgetting that the code even exists."

Now, a new discipline is emerging on top of that foundation: Agentic Engineering. Instead of treating AI as a clever text box, teams are beginning to design, orchestrate, and govern entire systems of autonomous agents that write, test, and evolve code under structured human oversight.

This article walks through that evolution and outlines what Agentic Engineering means for the future of software.

What exactly is vibe coding?

Vibe coding is an AI-assisted programming style where you describe what you want in natural language and let an LLM-powered environment generate and modify the code.

The term comes from a viral 2025 post by Andrej Karpathy, co-founder of OpenAI and former head of AI at Tesla. He described "a new kind of coding" where you "fully give in to the vibes, embrace exponentials, and forget that the code even exists" while an LLM such as Cursor's Composer writes and refactors the entire project. In his own words, he accepts all suggestions, barely reads diffs, pastes error messages back into the model, and lets the codebase grow beyond his usual comprehension -- which he explicitly frames as acceptable for "throwaway weekend projects."

A 2026 overview notes that this approach quickly spread beyond his personal workflow: within a year, "vibe coding" had a Wikipedia entry and was being used by a very large share of U.S. developers for rapid prototyping and side projects. The core idea is not just AI assistance, but a shift of attention away from the code and toward the feel of the product: you describe, see something, react, and iterate conversationally.

The limits of vibe coding in professional environments

For experimentation, hackathons, and quick UI ideas, vibe coding is incredibly powerful. But its very strengths become liabilities when you move into production:

  • Lack of deep understanding. A core part of the definition is that the developer often does not fully understand the generated code, leaning instead on tests, manual sanity checks, and further prompting to steer behavior.
  • Fragile reasoning and architecture. LLMs are excellent at local edits and small abstractions, but without a strong architectural frame they can generate tangled code paths, inconsistent patterns, and hidden coupling over time.
  • No built-in governance. Vibe coding typically treats the AI as a powerful but unregulated tool. There is no explicit notion of policies, safety constraints, or accountability beyond what the individual developer chooses to enforce.

As Simon Willison has pointed out, if an LLM writes every line but you still review, test, and understand all of it, you are using AI as an assistant -- not truly vibe coding. That distinction matters, because enterprises cannot afford opaque, ungoverned codebases, especially in regulated or safety-critical domains.

This is where agentic approaches come in.

From tools to teammates: what is agentic AI?

Agentic AI reframes AI systems as active teammates rather than passive tools. Instead of waiting for you to type code, an agent receives a goal, plans the work, chooses tools (like your repo, test runner, or CI pipeline), executes steps, observes feedback, and adapts its behavior.

A typical agentic loop looks like this:

  • 1. Receives a goal in natural language (for example: "Add a JWT-secured CRUD API around our orders table and wire it into the existing billing service.").
  • 2. Plans tasks, breaking the goal into subtasks (analyze schema, design endpoints, update auth middleware, write tests, update docs).
  • 3. Uses tools and context (codebase, issue tracker, shell, test suite, logs) to act.
  • 4. Observes results (test failures, lint errors, runtime logs) and refines the plan.
  • 5. Reports back through summaries, diffs, or dashboards that humans can review.

GitHub's definition emphasizes memory, tools, and autonomy as key ingredients: the model provides reasoning; the surrounding agent system turns that reasoning into purposeful execution across the SDLC. Google Cloud similarly describes "agentic coding" as an approach where agents plan, write, test, and modify code with minimal human intervention, behaving more like a skilled contractor than a passive autocomplete.

In other words, where vibe coding centers on experience ("the vibes"), agentic AI centers on structured behavior and goals.

Defining Agentic Engineering as a discipline

Agentic Engineering takes these agentic capabilities and turns them into a profession and engineering discipline, not just a buzzword.

A concise working definition from industry sources is:

Agentic Engineering is a software engineering discipline in which humans define goals, constraints, and quality standards, while AI agents autonomously plan, write, test, and evolve code under structured human oversight.

Organizations like the Agentic Engineering Institute (AEI) frame it as the fusion of AI systems design, cognitive architectures, and enterprise engineering. Their materials consistently highlight four pillars:

  • Agent orchestration and governance. Designing how many agents there are, what each is allowed to do, how they communicate, and how policies and guardrails are enforced at runtime.
  • Safety, reliability, and alignment. Ensuring agents' autonomous workflows stay within legal, ethical, and business constraints, and fail safely when things go wrong.
  • Human-in-the-loop architecture and accountability. Building explicit checkpoints where humans review, approve, or override AI-driven changes, with clear lines of responsibility.
  • Engineering rigor for production-grade autonomy. Applying the same seriousness as traditional software engineering: versioning, testing, observability, incident response, and post-mortems -- but extended to AI agents and cognition loops.

AEI, for example, talks about an Agentic Engineering Body of Practice (AEBOP) derived from hundreds of real-world deployments, and focuses on "production-grade, enterprise-grade, and regulatory-grade" systems -- a clear signal that this is about more than clever demos.

How Agentic Engineering changes software development

Agentic Engineering doesn't discard the Software Development Life Cycle (SDLC); it overlays an agent layer on top.

1. Teams design "agentic workflows", not just pipelines

In a classic DevOps world, you design CI/CD pipelines and human workflows. In an agentic world, you also design multi-agent workflows: which agents watch which signals, who can open PRs, who can label tickets, who can touch production configs, and how they coordinate.

Example patterns already emerging include:

  • A Spec Agent that refines user stories into technical requirements and acceptance criteria.
  • A Code Agent that implements changes for a given component or service.
  • A Test Agent that generates and maintains tests, monitors coverage, and investigates flaky tests.
  • An Ops Agent that watches logs, SLOs, and incidents, proposing remediations or opening tickets.

The orchestration logic -- when each agent wakes up, what context it gets, and how conflicts are resolved -- becomes a first-class architectural concern.

2. Developers shift from "typing code" to "specifying and supervising"

In vibe coding, the developer experience is often: describe vaguely, accept suggestions, and see what happens. In Agentic Engineering, developers and architects:

  • Define goals, constraints, and quality bars for agents.
  • Curate the tooling surface (which repos, APIs, and environments agents can touch).
  • Review proposed diffs, plans, and explanations from agents.
  • Tune reward signals or evaluation criteria so agents learn which behaviors are acceptable.

As one practical guide on agentic development puts it, the human focus moves toward architecture, problem-framing, and reviewing the AI's work rather than writing every line themselves.

3. New roles emerge: Agentic engineers, not just ML engineers

Agentic Engineering sits at the intersection of software engineering, ML, and operations. Practitioners need to be comfortable with:

  • Agent frameworks and orchestration (tool use, memory, planning).
  • Software architecture and API design.
  • Observability, evaluation, and safety patterns for AI systems.
  • Governance and compliance in domains like finance, health, or public services.

AEI and similar organizations explicitly position themselves as professional networks and certification bodies for this new profile -- much like how SRE, DevOps, or data engineering became distinct disciplines in earlier waves.

Concrete use cases: beyond toy examples

While a lot of discourse is still conceptual, there are already credible use cases where agentic approaches bring clear value:

  • Large-scale refactoring and migrations -- Agents can scan a large codebase, propose a migration plan (e.g., from one API to another), open scoped PRs, run tests, and iterate until all deprecations are resolved.
  • Continuous test generation and maintenance -- A Test Agent monitors code changes, coverage, and flaky tests, continuously proposing new or updated test cases in response to changes.
  • Documentation and knowledge management -- Agents keep architecture docs, README files, and runbooks in sync with the actual code and infra, catching drift over time.
  • Incident triage and remediation suggestions -- Ops-oriented agents watch logs and metrics, suggest likely root causes, and propose targeted patches or config changes for human review.
  • Internal platforms and self-service tooling -- Platform teams can expose high-level "capabilities" (spin up a new service, add an endpoint, onboard a new tenant), which agents then implement across many underlying systems.

These patterns echo what GitHub, Google Cloud, and others describe: AI moving from a context-less code generator to an embedded teammate in the SDLC.

Guardrails: safety, reliability, and governance

If vibe coding's risk is "I don't fully understand this weekend project," agentic systems raise the stakes: agents may touch production systems, customer data, or regulated workflows.

That makes guardrails non-negotiable:

  • Scoping and permissions. Least-privilege access to repos, environments, and APIs. Agents should not have blanket rights to production by default.
  • Runtime safety checks. Policies that block certain classes of actions (dropping tables, lowering security thresholds, exposing secrets) unless explicitly approved.
  • Evaluation and monitoring. Continuous measurement of agent behavior: success rates, regressions, incident correlations, and anomaly detection on actions taken.
  • Auditability. Full logs of agent reasoning, actions, diffs, and approvals so teams can run post-mortems and meet compliance requirements.
  • Human-in-the-loop approvals. Clear points where humans must approve changes -- for example, merging to protected branches, running risky migrations, or adapting security controls.

Industry guidance on agentic systems stresses that autonomy without discipline does not scale; the organizations that win will be the ones that treat governance and safety as first-class engineering problems, not afterthoughts.

How to get started with Agentic Engineering

For teams already experimenting with Copilot-style tools or vibe coding, a pragmatic path into Agentic Engineering might look like this:

  • 1. Map your workflows. Identify repeatable SDLC tasks (test updates, small refactors, dependency bumps, documentation updates) where agents could safely help.
  • 2. Introduce a single, tightly scoped agent. For example, a Test Agent that only operates in a non-production branch and cannot touch deployment configs.
  • 3. Add observability and review. Treat the agent like a junior teammate: track its contributions, require code reviews, and measure time saved vs. incidents created.
  • 4. Iterate to multi-agent orchestration. Once one agent is stable, layer in others (Doc Agent, Refactor Agent) and design how they coordinate.
  • 5. Invest in skills and standards. Train a small group as "agentic engineers" responsible for patterns, guardrails, and best practices; align with external bodies and guidelines where useful.

The goal is not to replace developers, but to multiply their impact while keeping humans firmly in charge of architecture, strategy, and accountability.

The future: beyond vibe coding, without losing the "vibes"

Vibe coding was an important cultural moment: it showed what happens when developers "give in to the vibes" and let LLMs handle the grunt work of code generation. It made the idea of conversational programming real, and it lowered the psychological barrier to letting AI touch our code.

Agentic Engineering is the next step: it keeps the creative flow and high-level intent of vibe coding, but adds the rigor, structure, and governance needed for real systems that must run 24/7, comply with regulations, and evolve safely over years.

In that sense, it is not hype but the logical maturation of software development in the age of autonomous AI agents: from typing code, to vibe coding, to designing, orchestrating, and governing fleets of intelligent teammates.

Sources and references

    From Vibe Coding to Agentic Engineering: The Future of Software Development | Versatik