The chatbot era is ending
A bot that answers questions saves nobody's payroll. An agent with tools and permission to act closes the ticket, files the record, and escalates the 5% it shouldn't touch.
MISSION FILE // STG-03 · AGENTIC AI
Agents that finish tasks, not conversations.
An AI agent wired into your stack that answers, files, and escalates — with an eval report that proves it works before a customer ever meets it.
- EVALS BEFORE CUSTOMERS
- GUARDRAILS ARE SCOPE
- HUMAN ESCALATION BUILT IN
00 / TELEMETRY
What's moving in agentic AI.
The currents worth steering by — and what they change about how this mission gets flown.
Evals became the buying criterion
Nobody serious ships an agent on vibes anymore. Accuracy measured on your real cases, a threshold agreed in writing, and a report you can show your board — that's now the baseline, and it's this pad's default.
Retrieval beats retraining
Your advantage is your data, not a custom model. RAG on your documents and systems gets current knowledge into the agent the day it changes, at a fraction of fine-tuning cost.
00 / WHY THIS PAD
What launching from here gets you.
One workflow, measured honestly
The workflow that hurts most gets picked and piloted in weeks. If the numbers don't clear the bar, you hear it from the builder before the next invoice.
Guardrails are a deliverable
Escalation paths, refusal behavior, and audit logs are line items in the scope document. The agent knows what it's not allowed to do.
Wired into your stack
Claude or GPT behind retrieval on your data, connected to the systems where work actually happens — not a widget floating over them.
It logs what it did
Every action traceable, every miss reviewable. That's how the agent gets better and how you stay comfortable letting it act.
00 / FLIGHT PLAN
How this mission flies.
T-4DISCOVER
Find the workflow where an agent pays its own invoice. Define the accuracy bar in writing.
T-3DESIGN
Data sources, tools, escalation rules — the agent's job description before its code.
T-2BUILD
Model + RAG on your data, evals running on real cases from the first week.
T-1SHIP
Pilot with real volume, eval report in hand, production hardening after the numbers clear.
00 / QUESTIONS
Asked before most launches.
How do I know it won't embarrass us?
Because it doesn't meet customers until the eval report clears the bar you signed off on. Below the bar, it stays in the hangar — and you see the misses, not a highlight reel.
What does a pilot cost in time?
2–4 weeks to a working pilot on one scoped workflow, then production hardening once the numbers hold. Small enough to be a decision, not a bet.
Claude or GPT?
Whichever wins on your evals. The architecture keeps the model swappable, so the answer can change when the models do.
Does our data train someone else's model?
No. Retrieval reads your data at answer time; nothing is used for training. Access stays inside your accounts, and the audit log shows every read.
00 / COMMS
One email starts the countdown.
Describe the workflow that hurts most. The eval plan comes back free.