4 AI Agent Examples Engineering Teams Are Actually Piloting

You've seen the term everywhere this year, but most "AI agent examples" lists mix a customer-support chatbot with an autonomous coding tool as if they solve the same problem. They don't. For an engineering team, an agent is software that plans a task, executes it in a real environment (shell, browser, file system), and reports back with a finished result, not a suggestion you still have to act on. Below are four agents actually built for engineering work, what each one gets right, and where the model still needs a human in the loop.

The distinction matters because the failure modes are different. An assistant that gets something wrong wastes your time reading a bad suggestion. An agent that gets something wrong can open a pull request against your main branch, install a dependency you didn't approve, or burn through a budget of "compute units" on a task that was never going to finish. Scoping matters more than capability.

You'll notice all four examples below live in different corners of the same map. Two act directly on a codebase (Devin, Replit Agent). One acts on the process work around engineering without touching code (Lindy). One is a general-purpose agent capable of writing code but not built around a repo at all (Manus). Picking the wrong corner for your task is the most common reason a pilot stalls.

Devin: Built to Ship PRs, Not to Chat

Devin, from Cognition, runs in its own cloud sandbox with a shell, browser, and editor. You assign it a ticket (migration, bug fix, scheduled maintenance) and it plans the work, writes the code, runs the tests, and opens a PR for review. It is scoped narrowly on purpose: no slide decks, no open-ended research, just engineering tasks with a code output.

Cognition reports Devin's PR merge rate climbed from 34% to 67% over a year, and that Devin now writes 89% of its own commits. That's a real improvement, and it's also a number worth reading carefully: a merged PR still went through a human reviewer. The agent didn't replace review, it changed what review looks like, from "did you write this correctly" to "did the agent's plan make sense for this codebase."

The part that's easy to miss in a demo: Devin does better on a codebase it has worked in before. It learns conventions and prior decisions over repeated sessions instead of starting cold each time. On a repo it's never touched, expect the first few tasks to look more like an onboarding period than instant output.

Where it earns its price tag is on volume. Cognition's own pitch is running many Devins in parallel on a single large migration, each one taking a slice of the work and reporting back with a reviewable PR, with integrations wired into GitHub, Linear, Slack, and Datadog so the work shows up where your team already looks. That's a different use case than "replace one engineer." It's closer to "clear a 400-file migration in a week instead of a quarter."

At roughly $500 per seat per month plus usage on top, Devin is priced for teams with recurring migration or maintenance work they can point a number at, not for a solo developer testing the waters.

Replit Agent: Prompt to Deployed App, No Local Setup

Replit Agent lives inside the browser-based Replit IDE. Give it a prompt and it scaffolds a full-stack app, wires up a database, writes and runs the code, and deploys it, all without you touching a terminal or configuring hosting. It's the fastest agent on this list from "idea" to "something a user can click on."

The tradeoff is scope. Replit Agent is strongest for web apps built inside Replit's own environment. Ask it for something that needs a VPC, an on-prem deployment, or a file-heavy research task, and you're outside what it was designed for, that's Manus territory, not this one. Pricing is credit-based and effort-based, which means a complex build can cost more than you'd guess before it runs.

The free Starter tier gives you limited daily agent credits to test the workflow before paying anything. Core, at $18 to $20 a month, adds usage credits and up to two parallel agent runs; Pro, at $90 to $100 a month, bumps that to ten parallel agents, useful if more than one person on a small team wants to prototype at the same time without queuing behind each other.

For a prototype, an internal tool, or a proof of concept you need running before a meeting, this is the agent that gets you there without a separate infrastructure conversation.

Lindy: What an Agent Looks Like When It's Not Touching Code

Not every engineering-adjacent agent writes code. Lindy runs recurring admin work: inbox triage, meeting scheduling, follow-up emails, reachable over iMessage or SMS as well as a web app. An engineering manager might point it at incident follow-ups or recurring status requests instead of code review.

It's worth including precisely because it's the opposite kind of example. Lindy has no concept of a pull request or a test suite. It automates the process work around engineering, not the engineering itself. If your mental model of "AI agent" is "something that touches my repo," Lindy is the reminder that the category is bigger, and that the right agent depends entirely on which recurring task you're trying to remove from a person's plate.

Pricing scales with how much of the work it owns: Plus starts at $49.99 a month for up to two inboxes and standard usage, Pro at $99.99 adds computer-use automation and a choice of underlying model, and Max at $199.99 covers up to five inboxes. None of that buys code review. It buys back the twenty minutes a manager spends every morning triaging what came in overnight.

Manus: The General-Purpose Agent, and Where It Breaks on a Real Repo

Manus operates a virtual computer: a real browser, terminal, and file system, planning multi-step tasks and returning finished files instead of chat answers. It can research a topic, write a report, and generate a working prototype in one run. Compared to Devin or Replit Agent, it's not scoped to engineering at all, which is exactly its strength and its limit.

Point Manus at a genuinely large, unfamiliar codebase and the cracks show. It doesn't carry the repo-specific memory Devin builds up over repeated sessions, and its credit-based task pricing gets expensive fast on anything requiring dozens of iterations against a real test suite. Its Wide Research mode, which fans a task out across many parallel sub-agents, is genuinely useful for "survey these twelve competitor APIs" work, less so for "fix this flaky integration test." It's best treated as a research and prototyping agent that happens to be able to write code, not as a substitute for a coding agent built around PR workflows.

Manus is now operated by Meta following the company's 2026 acquisition of the startup, which is worth knowing if data residency or vendor stability factors into your evaluation the way it would for any other engineering tool.

What Four Agent Examples Cost, Side by Side

Feature lists make these tools sound interchangeable. The pricing doesn't:

• Devin: around $500 per seat per month, plus usage-based compute units on top. No free tier. Priced for teams with recurring migration or maintenance work.

• Replit Agent: free Starter tier with limited daily credits; Core $18 to $20/month; Pro $90 to $100/month with up to ten parallel agent runs.

• Lindy: Plus $49.99/month, Pro $99.99/month, Max $199.99/month, scaling with inbox count and automation depth.

• Manus: credit-based tiers (roughly 4,000 to 40,000 credits a month depending on plan), with exact dollar pricing only shown as a live counter on its own pricing page rather than published outright.

The pattern: agents scoped tightly to one job (Devin on migrations, Lindy on inbox triage) charge a flat, predictable rate. Agents built for open-ended, variable-length tasks (Replit Agent's builds, Manus's research runs) charge on usage, which means your actual monthly cost depends on how ambitious your prompts are.

Why 88% of Agent Pilots Never Reach Production

Here's the number that belongs next to every one of these tools: 88% of agent pilots never reach production, across the enterprise agent projects surveyed for that figure, not just coding agents. Most teams run a convincing demo, then stall on the unglamorous part: scoped permissions, an evaluation loop, and someone whose job it is to own the thing when it's wrong.

The advice you'll see everywhere is "give the agent broad access so it's not constantly blocked." Skip it. The teams whose pilots actually ship give the agent narrow write access to one repo, one type of task, and a human-approved merge gate, then widen the scope only after a few weeks of evidence. Broad access on day one is how a demo becomes an incident report.

A pilot worth running for a month looks something like this: one repo, one recurring task type (a class of dependency bump, a specific bug pattern), a named owner who checks the agent's output daily for the first two weeks, and a hard number you're tracking, merge rate, hours saved, or time-to-first-PR. If you can't name that number before you start, you're not piloting an agent, you're demoing one.

Agent vs. Chat: Two Different Jobs on the Same Codebase

It's worth naming the thing none of these four tools do: answer a question about your code without acting on it. "Where is auth wired up in this repo" or "what changed in the payments module last sprint" isn't a task you want an agent to execute, it's a question you want answered in ten seconds, with a citation to the actual file.

That's a different job than shipping a PR or scaffolding an app, and conflating the two is where a lot of "should we adopt an AI agent" conversations go sideways. An agent that acts needs scoped permissions, an eval loop, and a rollback plan. A tool that answers questions about a codebase needs none of that, because it never touches the repo. Know which job you're hiring for before you pick a tool off this list.

This is also why the two categories aren't competitors. A team running Devin on migrations still needs a fast way to ask "why was this pattern chosen" before trusting the agent's plan on the next one. The agent handles execution; the question-answering layer handles the judgment call about whether execution is even the right move.

Which of These Agent Examples Is Worth Piloting First

If you have a specific, recurring engineering task with a clear definition of done (a dependency migration, a class of bug fix, scheduled maintenance), Devin is built for exactly that, and the price makes sense once you can point to hours saved. If you need a working prototype today and don't want an infrastructure conversation, Replit Agent gets there fastest. If the bottleneck isn't code but the process work around it, Lindy is worth a look before you assume you need a coding agent at all.

Skip Manus for engineering work specifically. It's a genuinely capable general-purpose agent, just not one built around your repo, your test suite, or your PR process, and that's the wrong tool for a codebase task even when the demo looks good. Pilot narrow, measure the merge rate or the hours saved, and only then widen access.