Your AI can write code fast, but it can also wander fast. That’s why we sat down with Jason Belk from Learning at Cisco to unpack spec-driven development, a simple idea with huge impact: write the rules and requirements first, then let your coding agent execute with far fewer surprises.
We talk through what “agentic coding” looks like in practice with Claude Code, including the trust and permission model of a local AI agent that can create files, run bash commands, and iterate on a real project. Jason explains how GitHub Spec Kit turns plain markdown and scripts into a repeatable workflow: start with a constitution that defines governing principles, then cycle feature by feature through specify, plan, tasks, and implement. Along the way we cover common gotchas like initializing in the wrong directory so skills never load, plus practical tips like using voice-to-text to improve prompts and choosing the right model tier when implementation quality matters.
We also zoom out to the bigger picture: why context windows break long builds, how keeping plans on disk helps the agent “re-ground” itself, and where the industry may be heading with small specialized models versus one giant general LLM. Jason shares learning resources too, including a Cisco U tutorial that frames spec-driven development for network engineers, and the Cisco AI Technical Practitioner course and certification, plus upcoming Cisco Live sessions.
Subscribe for more real-world AI workflows, share this with a teammate who keeps fighting prompt drift, and leave a review with the one automation project you want an agent to build next.
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