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Agentic Engineering

The entry point to the field: how it differs from vibe coding, how teams practice it, how to tell whether a team improves, and what changes at enterprise scale.

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Agentic Engineering 5

Our position. We hold that agentic engineering is engineering discipline applied to AI systems (opens in a new tab), not vibe coding. The gap between an AI demo and production (opens in a new tab) is discipline, not intelligence.

The values, principles and philosophy in Software Engineering stay the foundation. Agents did not change the operating model (opens in a new tab) of software engineering: they exposed whether a team had one.

How is agentic engineering different from vibe coding?

Vibe coding prompts until something runs, and agentic engineering decides upfront what “done” means and checks it. Unstructured AI-assisted coding works for a prototype and collapses on a codebase a team has to maintain. Systematic practice replaces it with written intent, curated context and deterministic verification.

How do teams practice it?

The practice has four parts: how the work is organized, what the agent sees, how the output is checked, and how the system is designed. Swapping the coding tool while keeping the sprint-based operating model captures only a fraction of the gain. Most agent failures are context failures, so context is the main lever over quality. Verification becomes the constraint once generation is cheap, and specifications and tests become part of the architecture because they are what the agent reads.

  • Methodologies & Frameworks — how agentic work is organized: spec-driven development, the agent-from-the-loop, and where old Agile breaks.
  • Memory & Context Engineering — managing the context window: retrieval, persistent memory, and the information architecture agents read.
  • Quality & Verification — making generated code trustworthy: review as the bottleneck, AI-TDD, deterministic gates, and evals.
  • Architecture & Design — how architecture work shifts up the stack when agents write the code, with specs and tests as the source of truth.

How do you know whether a team is getting better at it?

Measure the outcome the business cares about, not the share of code an agent wrote. A maturity model places an engineer on a path from occasional prompting to running agents as a system, and a small set of KPIs shows whether delivery moved.

What changes at enterprise scale?

At enterprise scale, context becomes a product that domains own and a shared platform serves. One central store fails the domains it does not understand, and one store per team fragments governance.

  • Software Engineering: the classical foundations agentic engineering builds on.
  • Product Management: the product side of the same shift, from tickets to written intent.
  • Software Factories: the standing production system teams build around their agents.
  • Tools: the coding agents, models and protocols that run this work.
  • Evidence: the studies and reports behind these claims.

Resources

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In this section

  • Architecture and Design

    Architecture matters more when agents write most of the code. The engineer's contribution moves to structure and judgment, and the artifacts that steer agents, such as specifications, tests, documentation and design systems, become parts of the architecture.

  • Federated Context Platform for Enterprise AI Agents

    For architects bringing agents into a large company: how context layers follow the org chart, what the gateway and catalog do, and where to start reading.

  • Memory and Context Engineering

    Context engineering is the work of curating the tokens a model sees at each step, and agent memory is what carries that context across sessions. Most agent failures are context failures, so this curation is the main lever an engineer has over agent quality.

  • Methodologies and Frameworks

    Agentic engineering methods redesign how humans and agents divide the work, now that humans no longer write every line. Swapping the coding tool while keeping the sprint-based operating model captures only a fraction of the gain.

  • Quality and Verification

    Verification is the constraint of agentic delivery: when agents write most of the code, generation is cheap and checking is scarce. A team ships as fast as it can prove the output right, through tests, deterministic gates, acceptance evidence and evals, not as fast as it can produce code.