Give us one person.
One hour a week.
$1,000.

You'll Learn

• • Document current-state processes with precision
• • Establish baseline metrics (time, cost, quality, volume)
• • Identify variation and waste in existing workflows
• • Research world-class benchmarks for comparison

You'll Build

• • Current-state process map
• • Baseline metrics dashboard

Guiding Question

"What is actually happening today?"

You'll Learn

• • Write unambiguous step-by-step procedures
• • Define terms, thresholds, and decision criteria
• • Create branching logic for edge cases
• • Version control your process documentation

You'll Build

• • Process source code document
• • Decision tree for exceptions

Guiding Question

"Could someone follow this without asking questions?"

You'll Learn

• • Design intake forms that capture required context
• • Build context compilers that structure messy inputs
• • Create prompt templates that consume structured data
• • Chain multiple AI steps into coherent workflows

You'll Build

• • Context compiler for your process
• • Intake-to-output workflow

Guiding Question

"What does AI need to know to run this?"

You'll Learn

• • Design validation checkpoints that catch errors early
• • Build approval workflows with clear escalation paths
• • Create automated QC rules for AI outputs
• • Balance speed with appropriate risk controls

You'll Build

• • Guardrail specification document
• • Escalation decision tree

Guiding Question

"What could go wrong, and how do we catch it?"

You'll Learn

• • Choose between Expert-run, Operator-run, and Agent-run modes
• • Design clean handoffs between humans and AI
• • Set up triggers and scheduling for recurring processes
• • Create runbooks for different execution scenarios

You'll Build

• • Execution mode decision matrix
• • Process runbook

Guiding Question

"Who runs this, and when?"

You'll Learn

• • Measure actual vs. baseline performance
• • Identify the next highest-leverage improvement
• • Build continuous improvement into the process itself
• • Create feedback loops that compound gains over time

You'll Build

• • Performance measurement dashboard
• • Improvement prioritization matrix

Guiding Question

"Did it work, and what's next?"

You'll Learn

• • Design multi-agent team structures
• • Define clear role boundaries to prevent overlap
• • Identify parallelizable vs. sequential work
• • Create coordination protocols

You'll Build

• • Multi-agent team blueprint
• • Role interaction diagram

Guiding Question

"Which agents need to work together, and how?"

You'll Learn

• • Design shared context that all agents can access
• • Create common rules and constraints
• • Define output contracts between agents
• • Build shared memory and state

You'll Build

• • Shared context specification
• • Inter-agent contract library

Guiding Question

"What does every agent need to know?"

You'll Learn

• • Design dispatch mechanisms for multi-agent work
• • Create check-in and sync points
• • Set WIP limits to prevent overload
• • Build convergence protocols for outputs

You'll Build

• • Orchestration workflow
• • Convergence checklist

Guiding Question

"How do we keep multiple agents in sync?"

You'll Learn

• • Design clean handoffs between agents
• • Create human-in-the-loop gates
• • Define escalation triggers and paths
• • Balance autonomy with appropriate oversight

You'll Build

• • Routing decision tree
• • Escalation protocol document

Guiding Question

"Where does a human need to approve or intervene?"

You'll Learn

• • Define metrics for multi-agent systems
• • Track throughput, quality, and intervention rates
• • Identify bottlenecks and failure patterns
• • Build continuous improvement loops

You'll Build

• • Multi-agent monitoring dashboard
• • System health scorecard

Guiding Question

"Is the system getting better over time?"