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AI coding agents and selfcoding technology

6 min read

From: aidotengineer

AI coding agents represent a significant advancement in software development, moving beyond simple autocomplete features to agents capable of assisting in, and even self-building, their own development [00:00:20]. These agents are designed to interact with development environments and continuously learn from human feedback and their own experiences [00:06:01].

Evolution of AI in Software Development

The field of AI development tools has seen rapid changes:

Augment Code’s Self-Building Agent

Augment Code, an AI research company building AI-powered dev tools, developed an AI coding agent that significantly contributed to its own codebase [00:00:28].

  • Over 90% of the agent’s 20,000 lines of code were written by the agent itself, with human supervision [00:01:27].

Capabilities of the Agent

The agent demonstrates various sophisticated capabilities:

Implementing Core Features

The agent can implement core features, including third-party integrations required for it to act like a software engineer, such as interacting with Slack, Linear, Jira, Notion, and Google, as well as managing the codebase [00:01:40].

  • Google Search Integration: The agent was able to add a Google search integration by examining the codebase to find the correct file and interface [00:02:08].
  • Linear Integration: When adding a Linear integration, the foundational model didn’t have the Linear API documentation memorized. The agent used its previously written Google search integration to look up the API docs and then implemented the feature [00:02:21].

Writing Tests

The agent can write tests, such as adding unit tests for the Google search integration, by using basic process management tools like running subprocesses, interacting with them, and reading output without hanging [00:02:37].

Performing Optimizations

Unlike common demos focusing on features and tests, this agent demonstrated the ability to optimize its own performance [00:03:08].

  • When the agent was slow, it was asked to profile itself [00:03:16].
  • It added print statements to its own codebase, ran sub-copies of itself, and analyzed the output [00:03:22].
  • The agent identified that loading and hashing all files in a user’s repository synchronously was a bottleneck [00:03:32].
  • It then added a process pool to speed up these operations and created a stress test to confirm the fix [00:03:40].

Example Interactions and Learning

The agent interacts using “tools” which are the third-party integrations or file editing capabilities [00:04:20].

  • Google Search: When asked if it can search Google, the agent identified its “Google Search” tool, performed a test query, and confirmed its capability [00:04:09].
  • Instrumentation with Logs: When asked to instrument its Google Search tool with logs and generate an example, the agent:
    • Used a retrieval tool to find the relevant file in the codebase [00:04:53].
    • Used its file editing tool to add print statements to the file [00:05:10].
    • Attempted to run a sub-copy of itself to view the logs but found missing Google credentials [00:05:24].
    • Used its clarify tool to ask the user for guidance on setting up credentials [00:05:37].
    • Upon receiving user feedback about the credential location, it used a memory tool to store this information for future use, demonstrating continuous learning [00:05:59].
  • Tasks Beyond Code: The agent can perform tasks outside of direct code writing but within the software development lifecycle:
    • Generating announcements from latest Pull Requests and posting them to Slack [00:13:00].
    • Creating data visualizations, such as a plot of interactive agent lines of code over time [00:13:28].

Challenges and Insights in Developing AI Coding Agents

Building these agents provided several key lessons and reflections:

Foundational Elements for Agent Success

Developing a powerful and scalable “context engine” and designing a good UI/UX were crucial foundational efforts [00:07:07]. Three critical elements for the agent to function effectively are:

  1. Access to Context: A robust context engine pulling from various sources like Slack or the codebase [00:07:33].
  2. Reasoning Capabilities: A best-in-class foundational model [00:07:41].
  3. Code Execution Environment: A safe environment to run commands within a customer’s system [00:07:45].

Debunking Common Assumptions

The development process revealed several common misconceptions about AI agents:

  • Assumption 1: L5 (Senior Software Engineer) Agents are Here: Twitter demos often show agents writing entire websites, but real-world professional software engineering is rarely “zero to one” and environments are messier [00:08:04]. Agents are not yet at this level, but they are still extremely useful [00:08:29].
  • Assumption 2: Agents Take Over Entire Categories of Tasks: Instead of building agents for specific categories (e.g., backend, frontend, testing), it’s more effective to think about improving agents across “levels of complexity” [00:08:42]. AI agents are general-purpose technology, improving across front-end, backend, and security simultaneously [00:09:02].
  • Assumption 3: Anthropomorphizing Agents: Agents have different strengths and weaknesses than humans [00:09:21]. An agent might struggle with math but implement a complex front-end feature much faster than a human [00:09:37].

Key Learnings

  • Onboarding the Agent is Crucial: Just like a new human hire, agents need to be onboarded to an organization’s specific knowledge [00:11:21].
    • A “knowledge base” (e.g., Markdown files) can be used to patch holes in a foundational model’s understanding (e.g., how to use Graphite, tool stack details, style guides) [00:10:28]. This knowledge is added to the agent’s context, allowing it to dynamically search and learn [00:10:59].
  • Code is Cheap, Explore More Ideas: With agents, the bottleneck shifts from engineering hours to good product insights and design [00:12:15]. The ability to “build everything at once” with agents means product managers can explore more ideas [00:12:06].
  • Natural Language and Codebase Awareness: The agent’s ability to understand natural language instructions (e.g., “instrument agent’s Google Search tool with logs”) and figure out which files to edit relies on excellent codebase awareness [00:12:41].
  • Multiplicative Context: Context comes in many forms (codebase, Slack, etc.), and having access to multiple sources is exponentially more useful than just one [00:13:48].

Testing and Optimization of AI Coding Agents

Agents can make mistakes, especially with hard-to-test scenarios like parallel programming and caches [00:14:57].

  • An example showed the agent writing a cache save function with a lock around JSON dumping to prevent race conditions but failing to implement a read-before-write, leading to data loss when multiple agents wrote in parallel [00:14:10]. This issue was missed because there wasn’t a sufficient test [00:15:08].
  • The Lesson: Sufficient tests are critical [00:15:11].
  • Impact of Tests: Enabling the agent to run tests improved its internal bug-fixing benchmark score by 20%, whereas a 6-month model upgrade only led to a 4% improvement [00:15:25].
  • Conclusion: Better tests enable more autonomy and make agents smarter [00:15:49].

Future of AI in Coding

The pace of AI improvement is accelerating due to a compounding effect where agents are beginning to help build themselves [00:16:11].

  • Code will remain the “spec of our systems,” but the relationship with it is changing [00:16:20].
  • Good test harnesses are becoming more important than ever, especially for less-tested parts of codebases [00:16:25].
  • The calculus of product development is shifting; if code becomes very cheap, the focus moves to good product work, gathering customer feedback, and building insights [00:16:37].

This technology is expected to positively transform the industry [00:16:50].

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