Early

What Really Makes an AI Agent... an Agent? Today, almost every AI tool is labeled an “AI Agent.” But not all of them live up to the name. At Early, we believe a true AI Agent checks five critical boxes: ✅ A well-defined task — a focused, purposeful mission ✅ Full autonomy — end-to-end execution without hand-holding ✅ High-quality output — accuracy and trust built-in ✅ Unprecedented speed — significantly faster than human workflows ✅ Consistency and reliability — because enterprise-grade tools can't afford surprises That last one might be the most important — and the hardest to achieve. And if an AI Agent can handle multiple tasks, across varying complexity? That’s not just an agent — that’s the beginning of an Agent of Agents. Next time you come across an “AI Agent,” ask: how many of these boxes does it check?

The EarlyAI tour is coming to Seattle March 28. Are you ready for it? Join the hundreds of developers that will get a chance to see how they can 🎯 automatically generate 30 working unit tests in 30 seconds! ⭐   Seattle Startup Summit https://lnkd.in/eUm4rh2i The Westin Seattle March 28, 8am-6pm #SeattleStartupSummit is on track to be the biggest startup event in Seattle. The theme is, of course, AI Dev Tools. A perfect fit for EarlyAI! Come visit us at booth #15 and see Sharon Barr present in the 1:30pm showcase. 😎 And we’ve also got some sweet swag to give away. A bunch of very interesting sessions with industry leaders including: 👉 AI Agents: Infrastructure, Usage and Evolution 👉 0 to 100M / The Scale Up 👉 The AI Stack And the chance to network with tech leaders, founders and investors --- Arize AI, Galileo🔭 , GMI Cloud, Pinecone, Statsig and many more... Hope to see you there!

🎤 Early is heading to the SF Awesome AI Dev Tools March event! We’re excited that Sharon Barr will be speaking at the AI Software Developer Gathering in San Francisco next week! 📢 Topic: AI Agents for Testing – How AI for testing is supporting developers in their journey 📅 When: Monday, March 24th @ 5 PM PST 📍 Where: GitHub Office, San Francisco 🔗 Registration: Link in the first comment With AI generating more code than ever, the challenge isn’t just writing it faster—it’s ensuring that code actually works and remains maintainable. This talk will cover: ✅ Why AI-generated code requires AI-driven test generation 🤖 The shift from AI-Assistants to Collaborative-AI-Agents in software development 🔍 How AI can autonomously generate, validate, and evolve test suites If you're in the SF Bay Area, come join us for a great discussion on how AI is shaping the future of software development and quality. A big thanks to Yujian Tang for organizing! Who’s coming? Drop us a comment 👇

Not all unit tests are created equal. Discover how mocking can transform your testing strategy! Unit tests are a developer’s first line of defense against bugs. They need to check and validate functionality, but even more important than that --- 🎯 They must ensure that your application behaves as expected in real-world scenarios. To do that, they need to uncover edge cases and unexpected behaviors by mimicking real-world scenarios and reliably simulating system behavior. And that’s where realistic and precise mocks come into play! 👉 Our latest technical blog post explores this issue with a detailed example (link in first comment) - testing the checkMaxNumberOfUsersSettings method in a user service. We chose this function because it performs multiple critical tasks and has so many dependencies that testing it requires accurate mocks that ⭐ simulate real behavior ⭐ separate concerns and ⭐ cover edge cases. Read the blog post to learn why you should use advanced mock generation techniques: 💡 Realism without complexity – Align closely with the actual implementation, ensuring tests are both realistic and easy to maintain 💡 Comprehensive coverage - Cover happy paths, edge cases, and error scenarios 💡 Error detection - Easily test error scenarios One of the reasons developers love using EarlyAI is that it automatically generates fully functional unit tests, complete with realistic and maintainable mocks. Try it out for free and generate your first tests within 60 seconds ⚡

The new code generation tools are making life easier - until the technical debt hits ⛔ It’s so easy to create code these days, but reality is quickly catching up to that dream... GitClear‘s AI Copilot Code Quality report analyzed more than 200 million lines of code showing that: 📉 All those code gen quick wins have resulted in a clear decline in code quality: ❗10x more code duplication compared to two years ago ❗ Much less code reuse than ever before (=more redundant systems) So many long-term consequences that people are not thinking about: ❌ Reduced delivery stability ❌ Dramatic escalation in technical debt ❌ Long term maintenance burden, especially for long lived repos ❌ More time developers waste on debugging code and resolving vulnerabilities ❌ Defect remediation and refactoring may soon dominate developer workloads ❌ Financial implications such as more cloud costs, more bugs, more resources... 🎯 Development teams need to put in place some checks and balances. From the article: “Testing becomes a logistical nightmare, heightening the developer’s operational overhead.” One immediate solution is to use AI agents, which provide code testing at scale, alongside your AI code assistants. So go check out Early!

Honored to see Early entering Insight Partners prestigious list of Developer Platforms for testing tools as we step into the era of AI Agents for testing! When you hear the word "AI Agent," ask yourself: ➡️ Which human task is this agent taking off my plate? And what is the quality of its output? ➡️ Is it truly autonomous, or does it still require significant intervention? (Assistant vs. Agent) ➡️ Does it provide enough visibility and control into the process? ➡️ How does it free me up to focus on higher-level, more complex work? And if it’s a task you hate doing (like testing), even better. That’s where AI Agents will truly shine. But here’s another the key insight: Even though AI Agents are the future, trust needs to be built. A fully autonomous agent might be capable of doing 90%-100% of the work, but developers still need a great experience to build confidence in its results. Would you step into a car with no steering wheel or brakes today? Or would you rather see it drive safely on its own for another billion miles first—while still having some control along the way? Excited for what’s ahead!

Test-Driven Development works—so why isn’t everyone doing it? Here’s how AI is making TDD a breeze. Test-Driven Development (TDD) ensures high-quality code by having developers write unit tests before implementing functionality. It encourages developers to think critically about software requirements, leading to cleaner, more modular code. The TDD cycle: 1️⃣ Write a test for a specific feature 2️⃣ Run the test (which initially fails) 3️⃣ Write the minimum code necessary to pass the test 4️⃣ Refactor the code to optimize performance 5️⃣️ Repeat the process until the software is complete The benefits of TDD include: ✅ Improved code quality ✅  Early bug detection ✅ Enhanced refactoring confidence ✅ Better design practices ❓But if it works so well, why do fewer than 5% of developers consistently adopt it? ❗Mostly because it requires significant manual effort, making it tedious and time-consuming. 💭 Wouldn't it be nice if you could streamline the TDD process, eliminate its common pain points, and even enjoy the process? This is where EarlyAI comes in: ⭐ Automated test creation ⭐ Edge-case coverage ⭐ Time efficiency ⭐ Accessibility Learn more about using AI to simplify and accelerate TDD by reading our full blog post ( 👉 link in first comment). It includes a live step-by-step example that walks you through the entire process!

🥱 Tired of wasting time on the boring process of writing tests? What if you could get a full test suite just by describing your expectations in a comment? 💥 ⭐ Lorenzo Zarantonello recently wrote an article on how to boost TDD (Test-Driven Development) using EarlyAI—turning comments into unit tests in one click! He put it perfectly: “Many claim big benefits once you get used to it. But most developers never even bother trying TDD. Most say it’s too time-consuming and boring. So here’s a challenge: use AI to write tests for you—before you start coding.” 💡 Lorenzo used EarlyAI to generate unit tests for a new component simply by adding comments. A few seconds later, he had a fully functional test suite, ready to go. As he summed it up: "EarlyAI is like Bolt.new for testing!" ⚡ 🙏 Huge thanks to Lorenzo for sharing his experience! 👉 Check out the full article for step-by-step examples (link in the first comment).

Your code coverage is 80%? Congrats… but your tests might still be useless. For years, code coverage has been the go-to metric for testing. The idea? The more lines of code covered, the better your tests. But here’s the problem: 🚨 90% code coverage does not mean high-quality tests. 🚨 0% code coverage doesn’t necessarily mean bad code, just that if you break it you won't have a clue. I’ve seen teams aim for high coverage, only to realize their tests weren’t actually catching or preventing real bugs. So what should we really measure? ✅ Do your tests actually catch and prevent bugs? ✅ Do they cover real-world use cases—both happy paths and edge cases? ✅ Can your tests detect unexpected mutations or regressions? Here is our attempt to define a new way to measure the tests quality of the tests themselves: 🔥 EQS (Early Quality Score) 🔥 Instead of just checking how much of your code is covered, EQS factors in test quality with three key dimensions: Code Coverage – What % of your code is tested? Mutation Score – How well do your tests detect real code changes? Scope Coverage – What percentage of your public methods have unit tests and 100% coverage? This takes test quality to the next level, answering the real question: Are my tests actually protecting my code? We’ve been using EQS internally at Early, and the insights are game-changing. It helps us evaluate our technology for high-quality test generation, spot gaps, and improve test effectiveness. What are your thoughts? Do you have other ideas to measure the quality of the tests themselves?

How can you tell if your tests are really protecting your code? Last week we wrote about EQS (Early Quality Score) which provides a clear indication of whether your tests are actually protecting your code. ❕ Reminder: EQS score 🟰 Code Coverage ❎ Mutation Score ❎ Scope Coverage (Scope coverage = how much of your methods have unit tests with 100% coverage) Today, we’ll do a deep dive into the second component – Mutation Score which evaluates whether your existing tests are effective = able to identify defects in your code. Mutation testing works by introducing small changes ("mutants") to the source code and then running the existing tests to see if they detect the changes: 1️⃣ Generate mutants - create multiple versions of the original code each with a slight modification (for example, changing a logical or mathematical operator and altering a constant value or a conditional boundary) 2️⃣ Run tests on the mutated code - test each mutant using the existing test suite to see whether the changes are detected (i.e., cause the tests to fail). Detected means killed, not detected means survived. 3️⃣ Analyze the results - Killed mutants vs survived mutants. There are other types of “not killed” mutants, like no coverage, timeouts, and errors which are beyond our scope today. 4️⃣ Calculate the mutation score - the simplified formula is ((killed mutants + time-out mutants) / (all mutants – error mutants)) * 100 💡 Important: High code coverage does not always result in a high mutation score! 💡 This is just one reason why you shouldn't rely on code coverage as the only way to evaluate the effectiveness of your tests. 💡 But using code coverage + mutation score is not enough either, which is why Early has completely rethought the way we approach test quality with EQS, and why you should too!   To review our complete analysis together with specific test examples, read the full blog post 👉 link in the first comment