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Case Study

Agents in Production

A production agent wired into a flow of tools and sources — fetching from the web, reading a datastore, running code, and writing output as the surrounding pipeline routes its work

Role

  • Staff Software Engineer
  • Production AI / Agents

Stack

  • Claude
  • Google Agent Development Kit (ADK)
  • Vertex AI
  • Firecrawl
  • Linear
  • Python
  • Laravel

These are the agents that act while a user watches. Search across the University of Maryland answers from 200+ of its own sites, every claim carrying the page it came from. An ingestion agent turns a single URL into draft-grid entries. A triage agent routes incoming work and drafts its assessment. Content agents write alt text and copy inside the publishing pipeline.

They all live in the product path, where a wrong answer reaches a person directly — so the discipline is different from building software. Not requirements and validation gates, but grounding so an agent can't invent, sourcing so every claim is traceable, and a human in the loop wherever being wrong has a real cost.

Answer With Sources, Not Guesses

Search Grounded in the Institution's Own Corpus

Proposed unified search, two views. View one — the user interaction across services: a visitor query reaches the search page, which injects a UMD preamble, sends an authenticated query to the composed search services (Vertex AI, a Pin API, and a keyword index), receives a cited summary with ranked sources, dedupes citations, appends a telemetry record to a cost log, and renders results. View two — how the services compose a response: the search page fans the query to Vertex AI, the Pin API, and the keyword index in parallel, whose outputs merge into one ranked, composed result set returned to the visitor.

University of Maryland's search runs on a Vertex-indexed corpus spanning 200+ umd.edu sites. It answers questions across the institution — and every answer carries the pages it came from.

  • Retrieval grounded in a Vertex AI index, not the model's open memory
  • Answers cite the umd.edu pages they were drawn from
  • 200+ sites span the institution as one search surface
  • Grounding is the guardrail — no source, no claim
See the UMD platform

From a URL to a Draft Grid

Ingestion as an Agent

A hand-drawn diagram: a website on the left feeds an n8n and Firecrawl workflow in the middle, which outputs structured data on the right.

Draft Slot's ingestion agent takes a single URL, fetches the rendered page through Firecrawl, and parses it into structured entries — team, player, and position — placed onto the draft grid.

  • A URL goes in; Firecrawl renders the page the way a browser would
  • The agent parses team, player, and position into typed entries
  • Entries land on the draft grid, ready for a human pass
  • Structured extraction, not freeform text — the output has a shape to check
Read the Draft Slot case study

Triage at the Front Door

The Agent Drafts, a Human Decides

Three triage agents in sequence — an intake agent that collects and prepares the request, an assessment agent that scores priority and routing, and a response/routing agent that integrates with CRM, ticketing, and APIs.

Incoming requests meet a workflow agent before they meet a person. It parses each request, routes it into Linear, and drafts a value and roadmap-impact assessment for human triage.

  • Requests parsed and routed into Linear automatically
  • Each arrives with a drafted value and roadmap-impact assessment
  • A person makes the call — the agent prepares the decision, it does not make it
  • Human-in-the-loop by design: the draft is a starting point, not a verdict

Content Agents in the Pipeline

Built on Google's ADK

The Google Agent Development Kit (ADK) mark — a robot face above code brackets.

Alt-text and marketing-copy agents ship inside the platform pipeline, built on Google's Python Agent Development Kit. They generate at the point of publishing — and what they produce clears review like any other content.

  • Alt-text agents draft accessible descriptions in the publishing flow
  • Marketing-copy agents draft at the point of need
  • Built on Google's Python ADK, deployed in the platform pipeline
  • Generated content meets the same review bar as anything written by hand

Why This Matters

Production Is a Trust Problem

An agent that builds software has a reviewer — me. An agent in production answers a user directly, with no one standing between its output and their screen. That single difference changes the whole discipline.

So these agents are built around their failure modes: grounded so they can't invent, sourced so every claim is traceable, and gated by a human wherever being wrong has a real cost. Velocity matters — but in the product path, trust is the product.

Read the agentic engineering practice