Senior Software Engineer II - Agentic Intelligence

Honeycomb is a developer-tools company focused on observability. The Senior Software Engineer II will design, build, and maintain production-grade AI agents that investigate, reason over, and act on live observability data in Canvas, while extending related agent surfaces and evaluation capabilities.

Responsibilities:

• Design and deliver production-grade agents. Build agents that investigate, reason, and act on live observability data inside Canvas. These agents must be trustworthy to engineers in high pressure situations, including mid-incident. Take one from rough first version to something that holds up under production traffic
• Own the agent work; support the whole product. Scope, build, ship, and maintain the agents including the evals that tell you whether they got better or are just different. This role is agent-focused and also includes some fullstack development
• Build agents only Honeycomb can build. Use a data store that returns high-cardinality queries in seconds to reason over signal a conventional backend can't serve at this fidelity correlating across services, drilling into a single trace, comparing before and after a deploy
• Extend the surface, and decide what's next. Ship new capability into Canvas, the MCP server, and Canvas Skills memory, spatial awareness, a faster Bedrock loop and make the case for what comes after with working code. Distinguish hype from signal in a field with plenty of both
• Define what "good" means for agents here. Set the bar: measurable against real evals, maintainable, and honest about their limits

Requirements:

• You've shipped LLM-based systems people relied on in production not demos, not fine-tuned models in a research context
• You know where agent systems break and how to design around it
• On a small team there's no handoff queue
• You can take something from rough prototype to production-grade without needing someone behind you to do the durable engineering
• You have informed opinions about what's shifted in agent design in the last six to twelve months that would change how you'd build today
• You understand how a fast, high-cardinality data store changes what an agent can reason about, and how to design for that
• You can look at what the agent layer does today and see what it should do next and make that case with a prototype, not a deck
• Engineers are your users; you'll ramp faster with fluency in that world, and the work is better
• Familiarity with eval frameworks, agent tooling, RAG, and prompt engineering