AI Engineer World's Fair 2026 — unofficial import demo

A front-door persona, a party of deterministic specialist agents, A2A between. Your support bot deflects half its tickets, then, soloing a problem it was never built for, confidently runs the wrong `kubectl` command. Most teams respond by rewriting the prompt. The real fix is a multi‑agent party of specialists. This talk gives you a production pattern that turns one over-leveled hero agent into a coordinated party of specialists you can trust on tier-zero infrastructure. Persona and ReAct agents make great heroes at the front door. Any team can copy one, paste it into their stack, and adjust the behavior in plain English. But if you send a lone hero to clear the dungeon, whether it is a deploy or an incident, a non-deterministic Reason-Act loop tends to loop, over-act, or punt back to a human. More prompts and more skills do not reliably level it up. Instead of soloing, keep the persona as the front-door face and give it a party: deterministic DAG specialists where the graph is fixed and the LLM is called only at decision points. For example, a deployment specialist can list rolling pods, choose the next tool, run it, read logs, and then diagnose the result. Each specialist is a class with one job and a narrow set of tools, and they coordinate over A2A for capability discovery and delegation across frameworks. Reliability and tighter least-privilege access become properties of the design, not something you try to bolt onto a prompt. You’ll leave with the pattern: where to draw the line between the hero and its specialists, how to shape a DAG specialist so it decides instead of flails, and where A2A fits as the seam between them, grounded in lessons from a tier‑zero fleet.