Agentic Engineering: What It Is and How It Actually Works

What is agentic engineering?

Agentic engineering is the practice of building software where AI agents handle the writing, testing, and iteration of code while humans set goals, design the architecture, and validate outputs. Instead of typing every function, engineers describe outcomes and let coding agents work through multi-step tasks autonomously — planning, implementing, running tests, and fixing failures until the work is done.

The shift is bigger than autocomplete. Tab-completion and inline suggestions assist the human who is writing the code. Agentic engineering replaces the human at the keyboard for substantial portions of the work. The job that’s left is scoping, reviewing, and steering — the things humans are still better at.

Andrej Karpathy and others have been pushing the term, but the practice predates the label. Teams shipping production software with agents in 2025 already had the playbook in rough shape. By 2026, it’s no longer experimental — it’s how a growing share of code gets written.

How is agentic engineering different from AI-assisted coding?

The simplest test is who holds the loop. With AI assistance, the human is in the inner loop — typing, accepting suggestions, running tests, fixing bugs as they appear. With agentic engineering, the agent runs the inner loop and the human supervises from a distance, mostly via the diff and the test output.

Mode	Human role	AI role	Iteration unit
Autocomplete	Author	Suggestion engine	Per keystroke
Chat-assisted	Author + reviewer	Pair programmer	Per snippet
Agentic	Architect + reviewer	Implementer	Per task or PR

Autocomplete makes you faster at writing code you’ve already decided to write. Agentic engineering takes a unit of work — “add a webhook handler that retries with exponential backoff” — and produces a working pull request without further input. They aren’t on the same scale of impact.

The cost of being wrong moves too. With autocomplete, a bad suggestion wastes a few seconds. With an agent, a misaligned task wastes half an hour and produces 800 lines of code somebody has to read.

Why does agentic engineering matter?

It matters because it changes the unit economics of building software. A team of three engineers running agents can ship the volume of work that used to require ten or fifteen — not because agents beat senior humans on hard problems, but because they handle the routine 80% of the work fast and at scale.

Three reasons it sticks:

Throughput. Agents work in parallel, around the clock, on tasks humans would otherwise queue. CRUD endpoints, schema migrations, test scaffolding, refactors, glue code, infrastructure changes — all of it ships faster.

Discipline. Agents are merciless at exposing weaknesses in your codebase. A vague spec produces a vague PR. Sloppy module boundaries make agents invent the wrong abstractions. Codebases that humans tolerate become unworkable when agents touch them. Teams that adopt agentic engineering quickly invest in spec rigor, naming clarity, and test coverage — investments that benefit humans too.

Capacity headroom. Smaller teams ship product surface area that previously required scale. A solo founder with a strong agent setup can credibly compete on roadmap with a Series A team. This isn’t theoretical. It’s already happening.

What does an agentic engineering workflow look like?

A workable agentic workflow has four stable stages — plan, dispatch, validate, integrate — that loop continuously. Implementations vary in detail, but the structure doesn’t, and skipping any of the four is how teams ship bugs faster than they ever did before.

Plan

A human (or a planning agent supervised by one) turns a feature request into a spec: requirements, design notes, a task graph. Tasks should be small, verifiable, and dependency-ordered. A good task says exactly what changes, where, and how to verify it.

Dispatch

The implementer agent picks up a task with the spec, the codebase, and a defined success criterion. It plans the implementation, writes the code, runs the tests, fixes failures, and produces a PR. Multiple tasks can run in parallel when their files don’t overlap.

Validate

A reviewer agent (and a human) checks the PR for correctness, style, and scope creep. Failures go back to the implementer with specific feedback. The validation step is non-negotiable — agents drift without it, and the drift compounds quickly.

Integrate

The PR merges, CI runs, and the next task starts. The cycle repeats. Specs that ship cleanly produce a retrospective that feeds the next planning round.

TurboKast is built this way end to end — Elixir control plane, Rust media plane, infrastructure-as-code, every line shipped through this loop. The split-language architecture we describe in our Elixir vs Rust piece was refined across dozens of these spec cycles, with friction points in the integration discovered by agents and fixed by agents.

What makes agents actually ship code?

The difference between a coding agent that ships and one that flails is rarely the model. It’s the scaffolding around the model — the feedback loops, success criteria, specs, conventions, and specialisation that turn a smart language model into a reliable engineer.

Tight feedback loops

Agents need to run their own tests, see their own type errors, and fix their own bugs. A loop that takes 30 seconds to compile and another 30 to test means twenty iterations per hour. A loop that takes five minutes means four. The math compounds quickly.

Clear success criteria

“Make it work” is a recipe for endless drift. “All tests in apps/turbokast/test/streaming_test.exs pass, and a new test covers the timeout case” is something an agent can verify and call done.

Specifications, not vibes

Agents are not psychic. The vaguer the spec, the further the agent drifts from what you wanted. A good spec front-loads the thinking: data flow, edge cases, file boundaries, test approach. The half-hour you spend writing one saves the four hours you’d spend reviewing what the agent built without it.

Conventions agents can read

A repo-level guide (CLAUDE.md, AGENTS.md, or whatever your tool reads), knowledge files describing domain boundaries, and rule files describing coding standards turn implicit conventions into something agents can follow. Treat your repo’s conventions like documentation aimed at a new hire who reads everything before writing anything.

Specialised agents

A single all-purpose agent works for small projects. At any real scale, specialists win: a planner that produces specs, an implementer that writes code, a reviewer that checks PRs, a debugger that diagnoses production issues. Each one tuned for its job, with the right model, the right tools, and the right context.

What are the common failure modes?

Agents fail in predictable, characteristic ways. Knowing the patterns is half the battle — every team adopting agentic engineering hits these failures, and the ones that ship are the ones that build guardrails against them rather than hoping the next model release fixes everything.

Drift

The agent solves a different problem than the one you described. Often subtle — the function works but solves an adjacent problem, or it solves your problem and twelve others you didn’t ask about. Caught by validation, prevented by precise specs.

Over-engineering

Agents love abstractions. Without a strict simplicity rule, you’ll get factories, registries, and pluggable architectures for things that should be a single function. Add an explicit “would a senior engineer call this overcomplicated?” check to your review step.

Plausible-but-wrong code

Agents will confidently invent functions that don’t exist, APIs that look right but aren’t, and constants that came from nowhere. This is fundamental to how language models work and it’s not going away. Mitigate with type checkers, real test runs, and reviewer agents that grep for invented symbols.

Silent guess-and-go

Faced with ambiguity, an agent will pick an interpretation and run with it. The agent doesn’t know that it doesn’t know. A behavioural rule like “stop and ask when confused” helps, but only if the agent is scaffolded to follow it.

Feedback erosion

When tests are slow, flaky, or hard to run, agents stop running them and start trusting that the code looks right. The codebase decays quietly until something breaks in production. Healthy test infrastructure is non-negotiable.

How do you build a codebase that’s friendly to agents?

The same things that make a codebase friendly to humans, taken further. Strong boundaries, comprehensive tests, fast feedback, written conventions, and clear domain documentation all matter twice as much when an agent is the one reading the code and an agent is the one judging whether the work is done.

Strong module boundaries

Agents change less when boundaries are clear. A function that lives in one place, called from a known set of places, is easy to modify safely. The same function spread across three modules with subtly different behaviours is a trap.

Comprehensive tests at multiple layers

Unit tests for module-internal logic. Integration tests for service boundaries. Acceptance tests for user-facing flows. An agent that touches code without a test layer that catches its mistakes will eventually break something nobody notices for a week.

Fast, deterministic feedback

CI that takes ninety minutes is a productivity tax on humans. For agents, it’s a hard ceiling on iteration. Aim for sub-five-minute feedback on the inner loop and aggressive parallelisation on the full pipeline.

Documented conventions

Every non-obvious convention in your codebase — naming, error handling, file organisation, telemetry — should be written down. Agents read what you write. They cannot infer what’s in your head.

Knowledge files for domain context

Agents do best when they can read a few short documents that orient them in your domain. Map the modules. Name the invariants. Describe the integration points. The investment pays back on every spec that touches the area.

What roles do humans play?

The myth is that agents replace engineers. The reality is that they shift what engineers do, away from typing and into judgement-heavy work where agents are still weak — system architecture, spec authoring, final review, operations, and the kind of taste calls that come from product instinct.

System architect. Someone has to decide where the seams go, which services own which data, and what shape the system should take. Agents are good at filling in within a structure, not at deciding which structure to build in the first place.

Spec author. A good spec is the highest-leverage artefact in agentic engineering. Writing one well requires deep judgement — what to include, what to leave open, where the risks live. This is hard to automate well.

Reviewer of last resort. Reviewer agents catch most issues. The ones they miss tend to be the ones that matter most: subtle correctness bugs, security issues, decisions that look fine locally but conflict with system-level invariants.

Operator. When something breaks in production, you need a human who understands the system end-to-end, can read logs and metrics, and decides what to roll back. Agents can debug — they cannot make judgement calls about user impact at 3am.

Taste. Product taste, design taste, and engineering taste come from humans, for now. Agents can implement a beautiful onboarding flow if you describe it. They can’t decide that the existing flow needs to be ten times simpler.

How do you start with agentic engineering?

Start small and tighten the system as you go. The teams that succeed treat the rollout as a series of short experiments, not a switch they flip. The first month is mostly about discovering what your codebase implicitly expects you to know — the things that aren’t written down anywhere — and writing them down so an agent can read them.

A practical sequence:

1. Pick a contained area. A handful of bug fixes or one discrete feature. Don’t start with a rewrite
2. Set up the basics. A repo-level conventions file, one or two clearly defined tasks, and a single specialised agent
3. Run a few cycles end to end. Plan, dispatch, review, integrate. Notice where the agent struggles
4. Tighten what broke. Specs more precise, tests more reliable, conventions written down, feedback faster
5. Expand. New domain, new agent specialisation, more parallelism

Don’t expect linear improvement. Throughput often drops in week one as you find the gaps in your conventions. By month three, you’re shipping at multiples of your previous pace because the gaps are filled.

Don’t skip validation. Agentic engineering without rigorous review is a way to ship bugs faster than ever.

Don’t assume what works for one team works for another. Codebases differ wildly. Conventions that work in a Rust media service won’t transfer unchanged to an Elixir control plane.

Where is agentic engineering headed?

Three trends to watch. Long-context models are getting big enough to hold a meaningful chunk of a codebase in working memory, tooling around agents is converging from bespoke setups to standardised libraries, and specialisation is replacing general-purpose agents with deeply tuned ones for specific roles.

Bigger context windows. Most of today’s coding agents struggle with codebases over a few hundred thousand tokens. Million-token windows make a real difference. Ten-million-token windows would change the game entirely — agents could hold whole services in memory at once.

Standardised tooling. Right now every team’s agent setup is bespoke. The patterns are converging — spec systems, validation gates, retry loops, specialised role definitions — and the best practices are starting to crystallise into libraries and frameworks.

Specialisation. General-purpose agents will give way to deeply specialised ones: a frontend agent that knows your design system, a database agent that understands your migrations, a security reviewer that checks for OWASP issues. Orchestration between specialists becomes the new platform layer.

The endgame isn’t fully autonomous software development. It’s a workflow where humans focus on the hard, judgement-heavy work — architecture, product decisions, edge cases, taste — and agents handle the rest. The tools, conventions, and team structures are still being figured out, and the teams investing now are the ones that will benefit when the dust settles.

If you’re curious what a fully agent-built production system looks like in practice, TurboKast is one. The streaming infrastructure described in our Elixir and Rust deep dive was specced, implemented, and shipped through agentic workflows. The product works because the workflow works — and the workflow works because we treat agents as a new kind of engineer who needs structure, not magic.