Best AI Agents for Coding in 2026: A Developer's Guide

Two years ago, "AI agent for coding" mostly meant autocomplete with a better memory. That era is over. In 2026, a coding agent reads your whole repo, plans a change across files, writes the code, runs the tests, debugs the failures, opens the pull request, and answers code review comments. Some of them do it in your terminal. Some do it in your IDE. A growing number do it in the background, while you are in a meeting or asleep.

At Tembo, we work with these AI coding tools every day. We build on top of them, watch teams adopt them, and see firsthand where they deliver and where they fall short. The hard part is no longer whether to use an AI agent for coding. It is picking the right one, or the right combination, and fitting it into the way your team actually ships software. That is true whether you are doing serious software development or vibe coding a side project on a Saturday afternoon.

This guide is based on current product capabilities, official pricing, and public documentation. It walks through the best AI coding agents in 2026, how they differ, and how to match them to your software development workflow.

What Is an AI Coding Agent?

An AI coding agent is a system that takes a natural-language instruction, reasons about a codebase, and executes multi-step coding tasks on it. That means reading files, editing them, running shell commands, executing tests, and iterating until a task is done. A coding agent is autonomous in a way that earlier AI coding assistants were not. You describe an outcome, and the agent figures out the steps.

In practice, that might look like tagging an agent in a Slack thread with "fix the flaky auth test in payments-service," and coming back thirty minutes later to a pull request with the fix, the test passing, and a commit message explaining what changed. That is agentic coding in action. Or assigning a Linear ticket for a dependency update across three repos, and waking up to coordinated PRs in each one. The value is not just speed. It is that the agent handles the well-defined, scoped work that fills up backlogs and slows down shipping, so your team can focus on the architecture and design decisions that actually require human judgment.

AI Coding Agent vs. AI Code Assistant vs. AI Code Completion

These three categories get mixed up constantly, and the distinction matters when you are picking a tool.

AI code completion is the original generation: inline code generation as you type, ghost text, single-line or multi-line autocomplete. GitHub Copilot's original tab-complete experience is the archetype. You are still driving every keystroke.

AI coding assistants are chat-first AI tools embedded in your IDE. You ask a question or request a change, the assistant proposes edits, and you accept or reject them. Cursor's chat panel and the original GitHub Copilot Chat fit here. You are still reviewing each suggestion individually.

AI coding agents are autonomous. You hand off a task, and the agent plans, executes, and verifies the work across multiple files and tools without needing a human in the loop for every step. Claude Code running in your terminal is an agent. So are Cursor's agent mode, GitHub Copilot's agent mode, and the growing set of "background agents" that run entirely off your machine, like the ones we’ve built at Tembo.

The line between assistant and agent blurs in practice. Most modern AI coding tools offer both modes. What matters for this guide is the agentic coding capability: can the tool take a multi-step task and finish it on its own?

How AI Coding Agents Work

Under the hood, a coding agent is a loop: a language model, a set of tools (read file, write file, run bash, grep codebase, run tests), and an orchestrator that feeds the model's output back as the next input. The agent reads your code, decides what to do, calls a tool, observes the result, and repeats until it believes the task is complete.

The interesting engineering is not the loop itself, but the context. A good coding agent knows which files matter for the current task, which ones to leave alone, and how your existing code conventions work. That context comes from a mix of repo indexing, retrieval, explicit instructions (rule files, docs), and sometimes a live LSP connection.

We've seen this repeatedly: the agents that feel the most useful are the ones that get context right, not necessarily the ones running the biggest AI models. An agent that understands your project structure and respects your conventions will write code that looks like your team wrote it. An agent that treats every task as a blank slate will produce code that works but feels like an outsider's contribution. That distinction matters more than benchmark scores.

The Best AI Coding Agents for 2026

There is no single best AI agent for coding. The market has split into at least four categories: per-developer terminal agents, AI-native editors, cloud-based autonomous agents, and orchestration platforms that run agentic coding tools for whole teams. These tools solve different jobs, so the table below mixes complementary options rather than direct substitutes.

Quick Comparison

Agent	Type	Autonomous Execution	Background Mode	Multi-Repo	Free Tier	Pricing
Tembo	Platform (orchestration)	High	Yes	Yes	Yes	$0 Free, $60/mo Pro, $200/mo Max
Claude Code	CLI	High	No	No	Claude plans	Usage-based (Claude plans)
Cursor	IDE	High	Limited	No	Yes (Hobby)	$20/mo Pro to $200/mo Ultra
GitHub Copilot	IDE + CLI	High	Yes (cloud agent)	No	Yes (50 agent mode or chat requests/mo)	$10/mo Pro, $39/mo Pro+
OpenAI Codex	CLI + IDE	Medium-High	No	No	Via ChatGPT Free	ChatGPT plans (Go $8 to Pro $200/mo)
Amp	CLI + IDE	High	No	No	No (pay-as-you-go)	Usage-based, zero markup
Cline	VS Code extension	Medium (human-in-loop)	No	No	Yes (BYOK)	You pay model provider
Aider	CLI	Medium	No	No	Yes (open source)	You pay model provider
Devin	Cloud agent	Very high	Yes	Limited	No	$20+/mo, enterprise tiers
Gemini Code Assist	IDE + CLI	Medium	No	No	Yes (individual)	Standard ~$19-23/mo, Enterprise ~$45-54/mo
OpenCode	CLI + IDE	Medium	No	No	Yes (open source)	BYOK or OpenCode Go ~$10/mo

For a deeper tool-by-tool breakdown of terminal agents specifically, see our detailed CLI tool comparison.

Tembo: Best for Background and Multi-Agent Workflows

Every other tool on this list is a single coding agent. We built Tembo to be the layer above: the orchestration platform that runs these agents autonomously, in the background, across multiple repositories, triggered by events in Slack, Linear, and GitHub. Tembo works with Claude Code, Cursor Agent, Codex, and other agents. You choose the agent and the model; Tembo handles execution.

Think of it this way: an individual agent is pair programming. Tembo is an agent delegation tool. Tag @tembo in a Slack thread and get a PR back. Assign a Linear ticket to Tembo and watch it ship. Run a single task that opens coordinated PRs across multiple repos, updating your API and client libraries in one coordinated change. Schedule agents to run on a cadence, or trigger them from webhooks. The automation library covers the repetitive work that piles up around production systems: auto-generating release notes from commits, triaging Sentry errors into the right Slack channel, keeping docs in sync with code changes, and writing PR descriptions.

This approach transforms reactive debugging into proactive maintenance and can improve code quality across the entire organization. Instead of waiting for problems to escalate, agents generate fixes and create pull requests before issues impact users. Tembo is explicitly agent-agnostic. It is not a replacement for Claude Code or Cursor. It is the infrastructure that lets those agents do work while you are not watching.

Model and agent support: Claude Code, Cursor, Codex, and others via MCP and full SDK integration.

Pricing: Free tier; Pro at $60/mo; Max at $200/mo.

Best for: Teams that want autonomous background execution, multi-repo coordination, and agents that run from the tools their team already uses.

Claude Code: Best for Terminal-First Developers

Claude Code is Anthropic's agentic coding tool. It runs in your terminal, reads your entire codebase, and executes multi-step tasks from a natural-language prompt. Install it with a single command, run claude inside any project directory, and describe what you want.

What makes Claude Code one of the best AI coding agents in 2026 is its bias toward autonomy. It does not just suggest code changes. It reads files, writes them, runs shell commands, manages git workflows, and iterates until the task passes. It integrates with GitHub via @claude mentions on issues and PRs and supports hooks, tool use, and project instructions for extending its behavior.

Model support: Anthropic's Claude models (Sonnet, Opus).

Pricing: Metered by tokens. Available through Claude plans (usage scales with plan tier).

Best for: Developers and software engineers who want a high-autonomy terminal agent and are already in the Claude ecosystem. Strong at multi-file refactors and git-heavy work.

Cursor: Best AI-Native IDE Experience

Cursor is a fork of VS Code rebuilt around AI-first workflows. It has chat, inline edits, and an Agent mode that runs longer autonomous sessions inside the editor. The keyboard shortcuts and UX are tuned for people who want AI tightly integrated into the act of editing, not bolted on as a side panel.

The thing Cursor does well is the feedback loop between a proposal and an accepted diff. You see exactly what changed in your existing code, you can edit inline before accepting, and you can run the agent across multiple files with visibility at every step. For developers who do not want to leave the editor, it is among the most polished agentic coding experiences available.

Model support: Claude, GPT, Gemini, and other frontier AI models selectable per task.

Pricing: Hobby (free), Pro ($20/mo), Pro+ ($60/mo), Ultra ($200/mo), Teams ($40/user/mo), Enterprise (custom).

Best for: Developers who live in an IDE and want an agent that feels like a native editing experience rather than a separate tool.

GitHub Copilot: Best for the GitHub Ecosystem

GitHub Copilot has evolved well beyond tab-complete. Its agent mode in VS Code and the Copilot CLI handle multi-step coding tasks autonomously, and the cloud agent (available on Pro and above) runs tasks in GitHub's infrastructure without tying up your local machine. Copilot's automated code reviews can also analyze PRs and improve code quality on every push.

The free tier is genuinely useful: 50 agent mode or chat requests and 2,000 completions per month, with access to GPT-5 mini and Haiku 4.5. Pro adds 300 premium requests for frontier models (including Claude Opus 4.6), unlimited agent mode with GPT-5 mini, and the cloud agent. If your team already lives in GitHub, the integration depth is hard to match.

Model support: Models from Anthropic, Google, and OpenAI, including Opus 4.6, selectable per task.

Pricing: Free (50 agent mode or chat requests/mo, 2,000 completions/mo), Pro ($10/user/mo), Pro+ ($39/user/mo).

Best for: Teams whose workflow centers on GitHub. Native repo, issue, and PR integration make it uniquely powerful for GitHub-centric organizations.

OpenAI Codex: Best Bridge Between ChatGPT and Code

Codex is OpenAI's coding agent, available as a macOS desktop app, terminal CLI, and IDE extensions for VS Code, Cursor, and Windsurf. It is designed for full engineering workflows: building features, refactors, migrations, and more.

Codex has its own plan lineup now, tied to ChatGPT tiers. The Free plan includes limited Codex access. Go ($8/mo) expands it. Plus ($20/mo) adds expanded Codex usage. Pro (from $200/mo) gets maximum Codex tasks. It also supports Automations for always-on background work like issue triage and CI monitoring. For vibe coding and rapid prototyping, Codex's chat-first approach makes it easy to write code from a single prompt. For a deeper head-to-head, see our Codex vs. Claude Code deep dive.

Model support: OpenAI models, including GPT-5 series.

Pricing: Via ChatGPT plans: Free, Go ($8/mo), Plus ($20/mo), Pro (from $200/mo), Business, Enterprise.

Best for: Teams already on ChatGPT who want a unified coding agent across desktop, terminal, and editor.

Amp: Best for Multi-Model Reasoning

Amp is a coding agent for terminals and IDEs (VS Code, JetBrains, Neovim, Zed). What sets it apart is aggressive multi-model use: Amp's "smart" mode uses Claude Opus 4.6, "deep" mode uses GPT-5.4 for extended reasoning, and "rush" mode picks faster, cheaper models for well-defined tasks.

Amp also includes several specialized sub-agents. The Oracle (GPT-5.4) provides a "second opinion" for complex debugging. The Librarian searches public and private GitHub repos for cross-repo context. The Painter generates images via Gemini. Amp supports skills, MCP servers, subagents, and code reviews. Thread sharing lets teammates learn from each other's agent sessions.

Model support: Claude Opus 4.6, GPT-5.4, fast models, selected automatically or by mode.

Pricing: Pay-as-you-go with zero markup on API costs. Minimum $5 credit purchase. Enterprise at 50% premium.

Best for: Developers who want the best AI model for each coding task without manual switching, and teams that value thread sharing and deep multi-model reasoning.

Cline: Best Open-Source Agent for VS Code

Cline is an autonomous agent that lives inside VS Code as an extension. It supports virtually every model provider (OpenRouter, Anthropic, OpenAI, Gemini, Bedrock, Azure, Vertex, and local Ollama) and operates on a strict approval model: every file change and every terminal command requires your explicit sign-off before it runs.

That human-in-the-loop posture is the whole point. Cline is the most conservative agent on this list by design. It includes browser automation for testing, workspace checkpoints for reverting experiments, and MCP support for building custom tools. If you want agent capabilities but your codebase is sensitive enough that you need to review every action, Cline is built for that.

Model support: OpenRouter, Anthropic, OpenAI, Google Gemini, AWS Bedrock, Azure, GCP Vertex, Ollama.

Pricing: Free VS Code extension. You pay your chosen API provider.

Best for: Developers who want full control, approving each step an agent takes.

Aider: Best for Git-Aware Coding

Aider is the oldest serious tool in this category and one of the reasons terminal-based AI pair programming caught on. It maps your entire codebase, supports 100+ programming languages, and automatically creates git commits with sensible messages as it works.

Aider's advantage is maturity. It supports nearly every LLM (Claude, GPT, DeepSeek, local models via Ollama), integrates with IDEs through a watch mode, and even supports voice-to-code for hands-free operation. The community around it is larger than for most of the commercial tools, which means there is a lot of tribal knowledge about how to use it well.

Model support: Virtually every LLM. Claude, DeepSeek, GPT-series, o-series, local models, and more.

Pricing: Free and open source. You pay your model provider directly.

Best for: Developers who want maximum model flexibility, a battle-tested tool, and git integration that actually works.

Devin: Best Fully Autonomous Agent

Devin, from Cognition, is the one built for hand-off-and-walk-away work. You describe a task in a Slack-style interface, Devin spins up a sandbox with a browser, terminal, and editor, plans the work, executes it, and reports back with a pull request. It runs in the cloud, not on your machine.

Devin's pitch is the highest ceiling on autonomy. It is designed for coding tasks you would hand to a junior engineer: "investigate this bug," "migrate this service to the new API," "write code for integration tests on this flow." It will not be the fastest option for a ten-line change. It is the option for tasks that would otherwise sit in a backlog.

Model support: Cognition's own agent stack, not user-selectable.

Pricing: Starts around $20/mo for individuals; enterprise pricing above that.

Best for: Teams who want to fully delegate medium-sized tasks and are comfortable reviewing completed PRs rather than supervising work in progress.

Gemini Code Assist: Best for Google Cloud Developers

Google's Gemini Code Assist covers IDE integration (VS Code, JetBrains, Android Studio) and the Gemini CLI for terminal-based agentic coding. A free individual version is available at codeassist.google. For teams, Standard and Enterprise editions are available through Google Cloud, with agent mode and Gemini CLI access included in both tiers.

Gemini's 1M-token context window is a real differentiator for the Gemini CLI and Code Assist alike. On large codebases, it holds more of your repo in working memory than most competitors, which changes what kinds of tasks it can handle without RAG or indexing tricks. The Enterprise edition adds code customization from your private repositories and broader Google Cloud integrations (Apigee, Firebase, BigQuery, Cloud Run).

Model support: Google Gemini models (Flash, Pro).

Pricing: Free for individuals. Standard at $19/mo (annual) or $22.80/mo (monthly); Enterprise at $45/mo (annual) or $54/mo (monthly) via Google Cloud.

Best for: Developers already on Google Cloud, teams working with very large codebases, or anyone who wants to start without upfront cost.

OpenCode: Best Free Open-Source Alternative

OpenCode is a rapidly growing open-source coding agent. It supports 75+ LLM providers and runs across terminal, IDE, and desktop. Multi-session support lets you run parallel agents on the same project, and the privacy-first design stores no code or context data.

OpenCode stands out for letting you authenticate with your existing GitHub Copilot or ChatGPT Plus accounts, so you can reuse subscriptions you already have. It is fully open source, with a large and active community. For developers who want a managed experience, OpenCode Go offers hosted access to rotating open-source models at around $10/mo.

Model support: 75+ providers via Models.dev. Claude, GPT, Gemini, local models.

Pricing: Free and open source (BYOK). OpenCode Go ~$10/mo for managed hosting.

Best for: Developers who want maximum provider flexibility, a privacy-first approach, and the ability to run multiple agents in parallel.

How to Choose the Right AI Coding Agent

With a dozen strong options and more launching every quarter, the question is not which agent is best. It is the agent that fits the way you work. These are the dimensions that matter.

CLI vs. IDE vs. Platform

If you live in the terminal and want the lightest-weight experience, pick a CLI agent: Claude Code, Codex CLI, Aider, Amp, or Gemini CLI. These agentic coding tools are fast, scriptable, and stay out of your way.

If you want AI woven into the act of editing, pick an IDE-native agent: Cursor, Cline, GitHub Copilot in VS Code, or Gemini Code Assist. You trade some autonomy for tighter feedback loops and richer visualization of code changes.

If you need agents running when you are not at the keyboard, you need a platform layer. That is where Tembo sits. Platforms wrap the individual agents and give them a home: background execution, multi-repo coordination, triggered automations, and shared visibility across a team. For more on this architecture, see our explainer on background agent architecture.

Cost and Pricing Models

These tools price themselves in several ways, and the right one depends on how you plan to use them.

Usage-based / BYOK (Claude Code, Aider, Cline, Amp) is cheap for light users and expensive for heavy users. You pay your model provider directly, with no usage quotas beyond what your API key allows. It works well when you can predict your workload or want maximum model flexibility.

Subscription (Cursor at $20-$200/mo, GitHub Copilot at $10-$39/mo, ChatGPT/Codex at $8-$200/mo) gives you a fixed monthly cost and, usually, a request quota. It works well when you want budget predictability, and you know your team will use it every day.

Credit-based platforms (Tembo, some enterprise tools) normalize cost across models. One credit represents a consistent unit of inference value regardless of whether the agent used a cheap model or an expensive one. That matters when you are running background agents and cannot control which model a given task will hit.

The honest tradeoff: pay-as-you-go gives flexibility but hides costs until the bill arrives. Subscriptions give predictability but punish light users. If you are new to AI coding agents, start with a free tier (Cursor Hobby, GitHub Copilot Free, Gemini Code Assist, Cline, Aider, Tembo) and upgrade once you know your pattern.

Open Source vs. Proprietary

Open-source agents (Aider, Cline, OpenCode, Goose) let you self-host, audit behavior, and avoid vendor lock-in. They are free to run, but you pay your model provider, and you are responsible for keeping the tool updated. For power users, open source also means you can customize agent behavior at the code level.

Proprietary AI tools (Claude Code, Cursor, Devin, Amp) are easier to adopt and usually have better out-of-the-box UX. You trade control for convenience.

Most teams end up using both: an open-source CLI for local work and a proprietary platform for the polished team-wide experience.

Team vs. Individual Use Cases

For individual developers, the question is which agent gives you the best editing and debugging loop for your stack. Claude Code and Cursor are among the best AI coding agents here in 2026, with Cline as the most conservative alternative and GitHub Copilot as the lowest-friction entry point for VS Code users.

For a team, the question changes. You need shared visibility, consistent quality, and the ability to hand work to agents without someone babysitting them. You need agents that respond to the tools the team already uses (Slack, Linear, GitHub), rather than requiring every developer to context-switch into a separate UI. And you need multi-repo coordination, because real engineering organizations do not live in single monorepos.

Here is what we have learned from watching teams adopt coding agents successfully: start with low-risk, high-visibility wins. Have the agent tackle clearly defined, scoped tasks where success is easy to measure: dependency updates, test coverage, and documentation that is always out of date. Maintain human oversight for everything. Treat agent-generated code the same way you would treat a pull request from a junior software engineer: review it carefully, verify the tests pass, and make sure the approach makes sense. Then expand gradually as trust builds. The teams that try to hand off their most complex architectural work on day one are the ones that lose confidence and revert.

That is the gap Tembo fills. Individual coding agents multiply a single developer's software development velocity. A team-level platform multiplies the whole organization's productivity by letting agents work in the background, respond to events, and coordinate across the whole stack. For a practical discussion of the adoption patterns that work, see our guide to challenges of adopting AI coding agents.

Real-World Benchmarks and Comparisons

Benchmarks in this space are noisy. SWE-Bench scores jump every time a lab ships a new model, and the gaps close fast. What is more useful for choosing a tool is the set of questions you can actually ask yourself about how an agent behaves on your code.

Setup Speed and Developer Experience

Time-to-first-useful-output varies wildly. Terminal agents like Claude Code and Aider can be running against a repo within minutes of install. IDE-native tools like Cursor require swapping editors, which is trivial technically but takes mental adjustment. GitHub Copilot activates instantly if you are already in VS Code. Platforms like Tembo add an onboarding step (connecting GitHub, Slack, Linear), but once connected, every new repo is essentially free to add.

The practical signal: how long until the agent does something genuinely useful against a project you care about? Under 15 minutes for CLI tools. Under an hour for IDE tools. Under a day for platforms, including the integration work.

Code Quality and Accuracy

Model choice accounts for most of the code quality variance across tools in 2026. Agents built around Claude Sonnet or Opus tend to produce more conservative, well-structured code. Agents built around GPT-series models tend to be faster and more willing to rewrite aggressively. Agents that are model-agnostic (Aider, Cline, OpenCode, Amp, Tembo) let you pick per task, which is the right design as model strengths shift every few months.

The other major variance is context engineering. Agents that index your whole repo, respect rule files, and pick up on existing conventions produce new code that fits the codebase. Agents that treat every task as isolated produce code that works but feels like an outsider wrote it. The best AI coding tools in 2026 are the ones that get context right, not the ones with the largest context window or the biggest raw model. For a closer look at quality gates, see our write-up on automated code review.

Cost per Task

A useful exercise: instrument the last ten tickets your team shipped and estimate what it would have cost to ship them with an agent. A typical bug fix runs from a few cents to a few dollars in inference. A medium feature that would take a software engineer half a day costs several dollars. A full migration runs tens of dollars.

Comparing that against developer time, the ROI is obvious for any non-trivial task. The real cost question is not per-task pricing. It is whether your team can review and merge AI-generated PRs fast enough to keep pace with how fast agents can open them.

The Takeaway

These tools represent more than just another category of software development tooling. They are changing the relationship between developers and code itself, shifting the job from writing every line to defining outcomes and reviewing results. The question is no longer whether to adopt them. It is how quickly and effectively you can integrate them into the way your team already works.

There is no single best AI agent for coding, and anyone selling you a single answer is oversimplifying. The right choice depends on where you work (terminal, IDE, browser), how much autonomy you want, what your budget looks like, and whether you are optimizing for one developer or a whole team.

For individual developers in 2026, Claude Code and Cursor are among the best AI coding agents in raw capability, with GitHub Copilot as the lowest-friction option and Cline as the most conservative. Amp stands out for multi-model reasoning. Aider remains the most flexible open-source choice. And if vibe coding is your thing, nearly all of these tools now support writing code from a single prompt describing what you want.

For teams, the answer is not a single agent. It is the combination of a good individual agent and an orchestration layer that makes those agents work in the background, across repos, triggered by the tools the team already uses. We built Tembo for exactly that layer. It runs any agent you choose, executes in the background, coordinates multi-repo changes, and turns Slack and Linear into a front door for delegated work.

The teams that will get the most out of agentic coding are the ones that treat agents the way they would treat any new teammate: give them clear tasks, set up the right guardrails, and review their work. The difference is that this teammate works on ten tickets at once and does not need coffee breaks. Start with the free tier and put your first background agent to work this afternoon.

FAQs

What is the best free AI agent for coding?

GitHub Copilot's free tier is the easiest starting point: 50 agent mode or chat requests and 2,000 completions per month in VS Code. Gemini Code Assist for individuals is free at codeassist.google, and Google's Gemini CLI is free for personal use within rate limits. Cline is free as a VS Code extension, and you bring your own API key. Aider and OpenCode are fully open source. Cursor's Hobby tier is usable for trying it out, but has tight limits. Tembo also offers a free tier that includes background execution and automations.

Which AI agent is best for coding in VSCode?

GitHub Copilot's agent mode is native to VS Code and integrates tightly with the GitHub ecosystem. Cline is the most widely used open-source agent inside VS Code, with a strict approval model that keeps you in control of every action. Cursor is technically a fork of VS Code and will feel nearly identical if you like the Cursor approach to AI. The right pick depends on how much autonomy you want the agent to have and which model ecosystem you prefer.

Are AI coding agents safe to use on production work?

Yes, with the right guardrails. The modern pattern is to keep agents in the same code review flow as human developers: they open pull requests, CI runs against their code changes, and humans merge. Sandbox their execution so they cannot touch production directly. Use rule files to encode your team's conventions. Start with low-risk tasks (docs, tests, dependency updates) and expand as the team builds trust. Every agent on this list runs through your normal review process if you set it up that way.

What is the difference between an AI coding agent and an AI coding assistant?

An AI coding assistant suggests changes; you accept or reject each one. An agent takes a task and executes it end-to-end, across multiple files and tools, without asking for approval at every step. Most 2026 tools offer both modes. The distinction that matters is whether the tool can hand you back a completed pull request, or whether it still expects you to be driving each change. Think of it this way: an assistant is a conversational assistant that responds when prompted, while an agent is an autonomous colleague that finishes the job.