Longhand combines practical learning with an Agent Operating System. Your people learn on real workflows, automate repeatable work, and track the return.
Agent workspace
Classification operations
Current batch
10 itemsProduct page research
Evidence gathered · 8 sources
Merchant classification
Evaluating evidence
Confidence review
Waiting for agent
Human review rule
Route only low-confidence classifications to an analyst.
Today’s activity
Batch size
10
Agent research
< 1 min
Workflow health
On trackEvery correction becomes feedback for the next run.
Illustrative view of an enterprise classification workflow
Built from the AI playbook used by leaders from
One adoption system
Longhand brings them together. People build judgment while agents take on the repeatable work. Both improve through the same feedback loop.
Learning Methodology & Curriculum
A guided learning path that moves from shared understanding to confident use on real workflows.
Agent Operating System
A shared workspace for automating workflows, reviewing exceptions, improving agents, and measuring the return.
Longhand AI agents gather the evidence and make an initial classification. Analysts review only the uncertain work. Every correction improves what happens next.
6–8 weeks
Traditional analyst training
Current process baseline
100 / day
Team classification output
Across 28 analysts today
2,000
Classification backlog
Work waiting to be completed
< 1 minute
Agent research time
Evidence gathered per analysis
“Instead of spending hours researching each classification, analysts can focus their expertise on low-confidence results and corrections.”
Outcome described in an enterprise AI agent workflow demo
Early benchmark
Hundreds of analyses per analyst, per day.
Projected from an eight-hour workday in the demo; production results should be validated against live usage and quality targets.
The operating model
Longhand turns an operations or sales process into a governed loop: queue the work, gather the context, run the agent, route decisions, and capture feedback.
Why this matters
Your team spends less time searching, copying, and updating systems—and more time resolving issues, advancing opportunities, and making decisions.
STEP 01
The agent pulls the next requests, records, or opportunities from the systems your team already uses.
STEP 02
It collects the customer, account, process, and source information needed to complete the work.
STEP 03
The agent follows your process to research, update, draft, evaluate, or recommend the next action.
STEP 04
High-impact or low-confidence work goes to the right person. Everything else keeps moving.
STEP 05
Team corrections and business results feed back into the workflow so quality and capacity improve over time.
Agent Operating System
Longhand gives operators and leaders one place to run agent work, inspect exceptions, capture learning, and measure whether a workflow is worth scaling.
ROI is measured per workflow
Connect steps, tools, source material, and decision rules so repeatable work can run consistently.
Approval and confidence rules send the right work to the right person without forcing manual review of everything.
Feedback becomes part of the workflow, helping the agent produce better work and need less intervention over time.
Monitor usage, quality, cost, completion, and human review so leaders can see which workflows are creating capacity.
Learning that reaches the work
Longhand combines guided instruction, independent practice, and hands-on support so people move from “I understand AI” to “I can improve this workflow.”
Live workshops
Teams learn the concepts together, then connect them directly to business priorities and workflows.
Self-paced courses
Role-relevant curriculum gives people the examples, practice, and reference material to keep moving.
Personalized coaching
Coaching helps people make better workflow choices, resolve blockers, and apply AI responsibly.
The result
Your curriculum creates capability. Your workflows reveal what to teach next.
Start with the work
We’ll map the current process, define the human review points, and identify how your team should learn, automate, and measure the result.