What Is Agentic Development?

Agentic development is the practice of using AI agents (not just AI assistants) to do real engineering work: writing code, running tests, fixing failures, and iterating toward a goal with minimal hand-holding. Industry write-ups converge on roughly the same definition: software development where a model takes autonomous, multi-step actions to complete a task (reading files, writing code, running tests, fixing errors, and iterating) without a human driving every step (MindStudio; Sourcegraph).

The distinction between "assistant" and "agent" matters, and it's not just marketing language. An assistant answers questions and suggests snippets you paste in yourself. An agent takes a task, breaks it into steps, executes those steps using real tools (a shell, a file system, a test runner), and keeps going until the work is done or it hits a wall it can't solve alone.

If you've used GitHub Copilot's inline suggestions, that's assistance: reactive, local, one line or block at a time. If you've told Claude Code or Codex "implement this feature, run the tests, fix what breaks," and walked away for ten minutes while it worked through the problem on its own, that's agentic development.

How Agentic Coding Actually Works

Under the hood, most coding agents run a loop: read context, decide on an action, execute it, observe the result, decide on the next action, repeat until the task is done or the agent gives up. This isn't a new invention specific to coding tools: it traces back to research like ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022), which formalized an agent's execution as a repeating cycle of Thought (an explicit reasoning step), Action (invoking a tool), and Observation (the tool's result, fed back into the next reasoning step). Anthropic's own documentation on how the Claude Code agent loop works describes essentially the same shape: the model evaluates a prompt, calls a tool, receives the result, and repeats until the task completes.

The pieces that make this work:

A planning step. Before touching code, a good agent breaks the task into a sequence of moves. Some tools show you this plan before execution starts. Claude Code and Codex both support explicit plan-review modes where the agent proposes a plan and a human approves it before any file gets touched; other tools generate and execute in the same breath. Whether you get to review the plan first is one of the biggest practical differences between agentic tools right now, and it's the difference between "the agent is doing what I expect" and "I'm about to find out what the agent decided." For a concrete framework for doing that review, see How to Review an AI Agent's Plan Before It Writes Code.

Tool use. The agent isn't limited to generating text. It can run shell commands, read and write files, execute your test suite, search the codebase, and call out to other services, often via a standardized interface such as the Model Context Protocol (MCP), an open protocol that both Codex and Claude-based tools support for connecting to external systems. This is what separates an agent from a chatbot with code formatting: a chatbot describes what you should do, while a tool-using agent does it and shows you the result.

A feedback loop. After each action, the agent looks at what happened (a test failure, a compiler error, a diff) and decides what to do next. Anthropic describes Claude Code's loop as three phases that blend together and repeat: gather context, take action, verify results. A single bug fix cycles through all three multiple times. This is what lets an agent recover from its own mistakes instead of handing you broken code and stopping.

Memory and state. Longer tasks need the agent to track what it already tried, what worked, and what the current state of the repo looks like. Weak agents lose this thread on long tasks and start repeating failed approaches. Stronger ones persist state more deliberately: Cognition's Devin, for example, was built around a "long-horizon reasoning" and persistent-memory approach specifically to handle multi-step engineering tasks that unfold over many actions rather than one exchange.

Why This Is Different From Autocomplete

Autocomplete predicts the next few tokens based on what you've already typed. It's reactive and scoped to the current line or function. GitHub's own documentation describes Copilot's inline suggestions as exactly that: ghost text you accept or reject as you type, well suited to boilerplate, CRUD operations, or extending a pattern that's already obvious from the code around it.

Agent mode is a different category of tool. As one developer-facing walkthrough of Copilot's agent mode puts it, you assign a high-level goal and the agent works through the steps a developer would otherwise do by hand: find the relevant code, plan the change, edit multiple files, run validations, and package the result, "less like a smarter autocomplete and more like handing a task to a junior teammate who works on a branch and comes back with a PR." Agentic coding is goal-directed: you state an outcome, and the agent works backward from it, deciding what files to touch, what order to do things in, and when it's actually done.

This shift changes what the developer's job looks like day to day. Instead of writing every line, you're increasingly reviewing plans, approving execution, and checking output. You act as an orchestrator rather than a typist. That's a real change in workflow, not just a faster keyboard. It's also why "prompt engineering" has partly given way to what some practitioners now call "loop engineering": designing the goal, the guardrails, and the checkpoints around an agent's loop rather than crafting a single perfect instruction. For a closer look at where an AI-assisted editor ends and a full agentic environment begins, see AI IDE vs. Agentic Development Environment.

The Problems Agentic Development Introduces

None of this is free. Running agents against a live codebase creates failure modes that didn't exist when a human typed every character.

Collisions. If an agent edits your working branch directly while you're also making changes, or while a second agent is running a different task, you get conflicts that are painful to untangle because neither side was reviewed before it landed. This is precisely the problem git worktrees exist to solve in general: they let you check out multiple branches from one repository at the same time, each in its own directory, without duplicating the underlying object store. Git itself enforces isolation at the branch level, refusing by default to let the same branch be checked out in two worktrees simultaneously, specifically to prevent the kind of conflicting, unreviewed updates that erase each other's work. As AI coding tools have proliferated, this old Git feature has found a new purpose: giving each parallel agent its own isolated checkout so that concurrent agent runs can't step on one another. For the mechanics of running several agents this way, see How to Run Multiple Claude Agents at the Same Time Without Conflicts and How to Manage Parallel Agents Without Losing Track.

No visibility before execution. Some tools generate and apply changes in the same step. You find out what the agent decided to do by reading the diff after the fact, which is a bad place to catch a bad decision: the code is already written, tests may have already run, and unwinding a plan you disagree with costs more than reviewing it up front would have. The stakes are higher still once real users depend on the branch an agent is touching: see How to Use AI Agents Safely on a Production Codebase.

Vendor lock-in. Committing to one model provider means you're stuck with its pricing, its rate limits, and its blind spots. Different models are better at different kinds of tasks, and switching should be a config change, not a rewrite. This has become enough of a live concern that most serious agentic tools now support at least two model providers, and increasingly a local option as well. How to Use Ollama for Private AI-Assisted Development covers that local option in practice, and The Hidden Costs of AI Coding Tools breaks down what single-provider lock-in actually costs.

One task at a time. Historically, single-session tools meant sequential work: a terminal agent like Claude Code, Codex CLI, or Aider runs one agent in one terminal, and you wait for it to finish before starting the next task. That's changing. Cursor, for instance, has introduced background and cloud agents that spin up sandboxed environments so multiple agents can work on the same repository concurrently, and cloud-native tools like Devin and GitHub's Copilot coding agent were built cloud-side from the start so a task can run without occupying your terminal at all. But the underlying tension remains: running several agents at once safely requires real isolation between them, not just the ability to open several chat windows. Cursor vs Unstoppable Code walks through how two approaches to that isolation problem actually compare.

Where Agentic Development Is Headed

The direction of the field is toward more parallelism and more supervision at the same time, which sounds contradictory until you see it in practice. Developers are running multiple agents on isolated branches simultaneously: Cursor's 2026 updates reportedly let users launch several agents on isolated Git branches for the same task and compare the resulting diffs before picking one, while also reviewing each plan before it executes and defining repeatable multi-step pipelines instead of one-off prompts that live only in a chat history. The rise of Model Context Protocol support across tools points the same direction: agents are increasingly expected to plug into existing infrastructure (CI systems, issue trackers, internal APIs) rather than operate in a sandbox disconnected from the rest of the engineering workflow.

Multi-step, repeatable automation is also borrowing directly from CI/CD conventions that predate agentic coding by years. GitHub Actions workflows are defined as YAML files with jobs (which run in parallel by default, or sequentially via explicit dependencies) made of ordered steps, a structure that's now being repurposed for agent orchestration: instead of one-off prompts, a workflow definition that says "run this agent, wait for this test job, only proceed if it passes." That same automation logic extends across repository boundaries too: see How to Orchestrate AI Coding Agents Across Multiple Repos.

If you're already running an agent in a terminal and hitting the ceiling of single-session work, that friction (no isolation between concurrent tasks, no review step before code lands, one model you can't easily swap out, one task queue instead of several) is exactly what the next generation of agentic tooling is trying to remove.

Where Unstoppable Code Fits In

This is the premise behind Unstoppable Code: run agents in parallel, each in its own isolated git worktree so nothing collides and your working branch stays clean, review the plan before any code gets written, and define reusable YAML pipelines for the workflows you run often.

Concretely, that means every task (yours or an agent's) runs in its own Worktree: an isolated git checkout and branch, so two agents working on the same repo at the same time can't overwrite each other's changes. Before an agent starts implementing, you see its plan and approve it, rather than discovering what it decided to do by reading a diff after the fact. And for the workflows you run over and over (build, test, review, fix-loop), you can encode them once as a durable, resumable pipeline instead of re-typing the same prompt every time.

You also aren't locked into one model. Unstoppable Code lets you bring Claude, Codex, or a local Ollama model and switch per task, in the same workspace, so a pricing change or a rate limit on one provider doesn't stall your work. By default, your code and project data stay local; Unstoppable Code doesn't send them to its own servers. One necessary caveat: whichever model provider you choose, Claude or Codex, does receive your prompts and code context, since that's how those models run. The one path that keeps everything, including inference, entirely on your machine is the local Ollama option, which is exactly the fully local path for codebases that can't leave the building.

For a fuller walkthrough of how isolation, plan review, and quality gates fit together in practice, see How to Supervise AI Coding Agents: Isolation, Plan Review, and Parallel Workflows.

If you're already running an agent in a terminal and hitting the ceiling of single-session work, that's exactly the friction this is built to remove.