Playbook

A Shopify agent that resolves order questions, starts returns within policy, keeps the catalog clean, and flags inventory risks, with permissions that keep refunds and pricing with people. This playbook builds it for merchants who cannot afford a mistake with a customer.

A Snowflake agent that answers business questions from governed definitions, executes as the asking user, shows its query, and refuses when evidence is missing. This playbook builds it so self-service analytics does not become self-service errors.

Billing operations on Stripe is a stream of failed payments, plan changes, invoice questions, and disputes. This playbook builds an agent that handles the routine within policy and prepares the rest for people, with restricted keys that keep refunds and payouts human.

A Workday agent that answers employees from their own record, validates onboarding data, and processes rule-based transactions, with privacy boundaries enforced by permissions rather than promises. This playbook builds it under the scrutiny HR data demands.

Every retrieval and document agent is only as good as its ingestion. This playbook builds the pipeline that takes documents from intake to index: extraction, structure, classification, chunking, metadata, permissions, quality gates, and updates.

A golden dataset is the team's shared definition of correct, and it is only as good as its design. This playbook builds one that covers what matters, verifies every outcome, and keeps growing from production so quality stays measurable.

Every prompt, trace, and evaluation case is a place personal data can leak. This playbook builds a redaction pipeline that detects and transforms PII by policy before it reaches models, logs, or datasets, and proves its coverage with evaluation.

Agent traces are where the truth about behavior lives: what was read, what was called, why a step was taken. This playbook builds the pipeline that captures, stores, and analyzes traces so failures are diagnosable, costs are attributable, and evaluation keeps learning.

Shadow mode is where the specification meets reality. This playbook runs an agent on live cases without letting it act, compares its proposals with what people did, turns every disagreement into a specification decision, and produces the evidence that justifies launch.

Text-to-SQL demos impress and production text-to-SQL disappoints, because the schema was never the problem. This playbook builds an agent over certified definitions that clarifies ambiguity, validates every query, executes as the user, and shows its work.

An analyst agent goes beyond answering a question: it plans an analysis, decomposes a change, compares periods and segments, and writes a narrative with every figure sourced. This playbook builds one on governed data with the review discipline the role demands.

Callers tolerate about a second of silence before they assume the line is dead. This playbook cuts voice agent latency stage by stage: budget, streaming, endpointing, model work, caching, tool calls, and infrastructure placement, with measurement at every step.