AI coding agents
12 moments from 12 videos · 30:27 min total
This playlist collects expert insights on building and deploying autonomous coding assistants. Learn how they automate refactoring and code generation to streamline your development workflow.
The promise and risk of AI coding agents
High-velocity AI adoption increases individual developer effectiveness but introduces severe software delivery instability.
Defining the capabilities of an AI coding agent
A coding agent embeds a large language model with tool-calling mechanics to actively evaluate tasks and execute workflows.
Training AI coding agents to utilize modern runtime capabilities
How to augment prompt context so coding assistants generate software relying on contemporary native APIs.
The reality of using AI coding agents in legacy codebases
Coding agents struggle to infer hidden constraints and invisible knowledge scattered throughout large enterprise applications.
Accelerating right of code workflows with AI agents
AI speeds up code reviews, deployment, and incident management to keep pace with increased code volume.
Expanding AI agents for code review and security scanning
Specialized software agents automatically manage code reviews, scan for security vulnerabilities, and handle repository merges.
Evaluating AI agents for iterative legacy code modernization
Using AI agents to analyze legacy applications provides an iterative, low-risk approach to handling difficult codebases.
Tracing agentic capabilities and step-by-step code execution
Observing what coding agents execute behind the scenes, including underlying bash scripts and token consumption.
Web tools sabotaging AI and Mozilla agent coding resources
A tool artificially slows chatbots while Mozilla proposes a code-sharing platform for AI agents.
Feeding structured software requirements into autonomous AI coding agents
Clean requirements improve the implementation plans of autonomous coding tools while aiding broader team cultural adoption.
Defining and measuring success for AI coding agents
Capturing transaction outcomes like acceptance, ignorance, and explicit rejection reveals true product viability.
Connecting reasoning engines to autonomous coding agents
Interfacing conversational generators with robust executable external APIs organically creates persistent autonomous frameworks capable of programming.
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