ProductAgent Builder
Upload the procedure. Get an agent that follows it.
Agent Builder reads a process document your team already wrote — a PDF or DOCX SOP — and assembles a working agent from it: the role it plays, the phases it moves through, the tool each step needs, and the rules it must follow, each cited to the line it came from. You review and approve before it runs.
One document becomes an agent — with a review gate in the middle.
Import the SOP your team wrote to onboard new hires. Trail builds the agent from it and shows you why it made every decision, before any of it can run.
- 01
Import the SOP
Drop in the process document — PDF or DOCX. Trail reads it structurally: the purpose, the ordered steps, the exceptions, and the point where each one needs a system or a person.
- 02
Agent auto-built
Trail outlines the agent's role, splits the procedure into phases, attaches the tool each step needs, and drafts the rules the process implies — each with the sentence it was drawn from.
- 03
Review & approve
Every proposed step and rule is explained and traced to its source. Your team edits, approves or rejects, and nothing the agent can act on goes live until someone signs off.
What Trail assembles
An SOP is written for someone who already knows the job — an agent needs all five spelled out.
A procedure doc assumes a person who already knows the tools and the judgement calls. Agent Builder makes each of those explicit, so the finished agent works the way your team documented — and every part of it points back to the text it came from.
Role
The agent's job · What is this agent responsible for?
A plain statement of its job, drawn from the document's purpose
Phases
The process, in order · What are the steps, and in what order?
The procedure split into ordered phases the agent moves through
Tools
Systems & actions · What does a step need to act?
The system or action wired to each phase — through the agents you already run
Rules
Policy, cited · What must it never get wrong?
The constraints the process implies, each cited to its line and approved
Approvals
Human in the loop · Where does a person decide?
The steps that pause for sign-off, and who owns them
From a line in the SOP to a step in the agent.
Trail reads the procedure, builds the step, and shows the sentence it rests on. Your team approves it before the agent can run it.
…where a rejected invoice indicates the cost includes an involuntary change and we will not pay the supplier, this ultimately amounts to a non-accrual state…
On a rejected invoice citing an involuntary change, set accrual to N — with the AR-hold tool attached and an approver on the step.
- Built from a PDF or DOCX SOP
- Role, phases and tools laid out for review
- Rules cited to document, page and line
- Human approval before anything runs
- Re-open the SOP to update the agent
- Runs on Claude, ChatGPT or your own stack
- Every run and approval audited
Consumed by the agent your team already runs.
Trail isn't a destination app. Point Claude, ChatGPT, Copilot or your own stack at one Trail brain through a native Model Context Protocol server, native retrievers, or plain REST and GraphQL.
Security built in, not bolted on.
Your data and your company brain never leave your boundary. Independently audited every year, with compliance controls enforced by the platform — not promised on a page.
The SOP, the agent and its rules stay inside your boundary — managed, single-tenant, or in your own VPC with storage pinned to a region.
SOC 2 Type IIOngoing security controls
GDPREU data protection
ISO 27001Global ISMS standard
HIPAABAA on enterprise plansAgent Builder questions.
What can I upload?
The process documents your team already has — PDF or DOCX. Trail reads the document directly; there's no template to fill in or flowchart to draw.
Where does each rule come from?
Every proposed step and rule includes a plain-English explanation and a link to the exact document, page and text it was drawn from. Nothing goes live until your team approves it.
Which agent does the finished agent run on?
The one you already use. Trail connects through a native Model Context Protocol server, retrievers for LangChain and LlamaIndex, or plain REST and GraphQL — so the agent runs inside Claude, ChatGPT or your own stack.
What happens when the process changes?
Re-open the SOP. Trail re-derives the affected steps and rules and updates the agent — and everything downstream that depended on them — with the same review step before anything new goes live.
See it built on your own context,
with your agents.
Bring one policy document and one real decision. We'll show you what Trail parses out of it, and where each piece came from.
Looking for your team's use case? See all solutions →