@jigyasagroverofficial: Partnered with @google to talk about the glow-up your agent needs to go from “cool demo” to production-ready 💅🏻 Four things I keep coming back to: ❶ Multi-agent orchestration. The biggest unlock isn’t a smarter model, it’s stopping one agent from doing everything. A root orchestrator that routes queries to specialized sub-agents (each with its own tools, instructions, and settings) scales and debugs far better than one giant prompt trying to be everything at once. ❷ MCP (or Model Context Protocol). This is the USB-C for agents — a standard way to plug tools and live data sources into an LLM without writing custom integration code for every single source. Once you’ve wired an agent to MCP, giving it a new capability is a config change, not a rewrite. ❸ Callbacks as a security layer. This is the part that gets skipped in most tutorials. ADK lets you hook into the agent lifecycle at multiple points — before the model runs, after it responds, before a tool executes — and each hook is a chance to intercept, validate, or block: user Input → before_model → LLM → after_model → before_tool → tool ❹ Guardrails, not vibes. Prompt injection detection, PII redaction, tool-input validation — these aren’t nice-to-haves, they’re what separates something you’d ship from something you’d only show in a demo. ADK gives you the scaffolding for all of the above out of the box — orchestration, MCP tool integration, callback hooks, session state — so you spend your time on agent design instead of plumbing. I recorded a short demo pulling all of this together using a simple 3 agent example — as a vehicle to show the architecture, not the point in itself. Find links to the video and the demo in the comments. If you’re building agents, curious what patterns you’re using for orchestration and guardrails.

Jigyasa Grover 📍 sf bay area

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Monday 20 July 2026 02:25:51 GMT