10x your agentic engineering
Upgrade from isolated prompts to a disciplined agentic engineering workflow.
Summary and audience Q&A
Agentic Development
This seminar makes a simple but important point: the current state of AI engineering is still early. A lot of people now have access to strong models and some form of harness, but that does not automatically give them a reliable way to build software with AI. The hard part is still the workflow around the model: how work is broken down, how tools are used, how context is loaded, how changes are verified, and how the system recovers when the first attempt is wrong.
That is why agentic workflows matter so much. The real leap is not from one model to another, but from isolated prompting to a structured loop where the model can inspect code, run tools, test behavior, verify outcomes, and hand work across sub-agents when needed. Right now, most teams still have to stitch that together themselves. They may have a harness, but they do not yet have a clean, dependable workflow layer that makes AI feel like part of a disciplined engineering system rather than a smart autocomplete tool.
That is what makes Nexus interesting. It is trying to solve the workflow problem directly by giving people something more complete than just access to a model or a harness. The goal is to make agentic development feel structured, practical, and usable in the real world. If that is the direction you want to explore, you can try Nexus.
Tips, Tricks, And Workflow Tools
These are the practical tools and notes that support the workflow:
| Category | Linked items | Why they matter |
|---|---|---|
| Coding harnesses | These connect the model to your workspace and tools. The right choice depends more on workflow fit and control than hype. | |
| Models | Different models fit different task sizes. Smaller and cheaper models are useful for quick tasks, while larger-context models are better for broad reasoning across a codebase. | |
| Voice input | Voice tools can speed up prompting and make interaction feel more conversational, though they can be tiring over long sessions. | |
| Workflow knowledge | Lightweight workflow instructions, MCP integrations, and reusable commands help teach the model how to work without loading everything into context every time. | |
| Browser verification | Browser tooling becomes much more powerful when agents can inspect the UI, reproduce issues, and verify fixes instead of editing code blindly. | |
| Parallel workspaces | Terminal multiplexers help run multiple agents or long-lived sessions in parallel without losing control of the workflow. | |
| Integrations | These kinds of integrations matter when the workflow needs direct access to GitHub or design artifacts, but they should be used selectively. | |
| Project memory | These encode conventions, architecture decisions, auth patterns, deployment flow, and other knowledge that the code itself does not explain clearly. | |
| Secrets and config | Security and secret management still matter. Secret-manager-backed workflows are preferable to raw .env sprawl. |