Automating Repetitive Platform Work with AI Agents
The author built a system of 7 specialized AI agents to automate repetitive tasks in their platform development work, including creating agents, dashboards, and connectors.
Why it matters
This approach demonstrates how AI can be used to automate repetitive, high-value engineering work, improving efficiency and consistency.
Key Points
- 1Developed a plugin with 7 AI agents to automate common platform development workflows
- 2Agents cover different phases like requirements gathering, architecture, and testing
- 3Agents learn from mistakes through a self-improvement feedback loop
- 4Focused on defining specific formats and patterns rather than generic templates
Details
The author, who builds things on the DevRev platform, found that many of their tasks followed the same repetitive patterns - creating connectors, dashboards, and other components. To automate this work, they built a system of 7 specialized AI agents using Claude, a large language model. The agents cover different phases of the development process, from gathering requirements to architecture and testing. The key insight was to be very specific about the formats and patterns the agents should follow, rather than using generic templates. This allowed the agents to closely match the team's existing workflows. The author also built a 'skill improver' agent that learns from mistakes made by the other agents, patching their knowledge and making the whole system smarter over time. The architecture consists of two main verticals - one for snap-ins/connectors, and one for dashboards/widgets. Each vertical has a PM agent, an architect agent, and a tester agent. The self-learning 'skill improver' agent ties the whole system together. In total, the plugin contains over 6,400 lines of agent instructions, packaged as a Claude Code plugin with 7 slash commands.
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