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This workshop decides how people will use the graph. In Workshop 1 the client wrote golden questions and named agent candidates. Here you turn them into the things users touch: assistants in chat, document templates for branded output, and agent flows for repeatable multi-step work. It matters for accuracy because the graph only answers well when the assistant speaks the client’s language. If people say “engagement lead” and the graph stores a WORKS_ON_PROJECT relationship with role = "Engagement Lead", someone has to write that down. Experio cannot guess it.

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At a glance

Assistants and AI Instructions need AI Managers write access. Agent flows and document templates need a staff account. See Roles & permissions.

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Before the workshop

FFE and Experio SMEs:
  • Sort the golden questions into three groups: answered in chat, answered with a document, needs a multi-step workflow. Most questions stay in chat.
  • Collect the vocabulary gaps from accuracy validation. Every golden question that failed with root cause “retrieval” usually points at a missing term mapping.
  • Read Assistants, Document Templates and the Agent Flows Cookbook.
  • Check that the models you plan to use appear in AI & Agents > Model Configurations.
Client homework:
  • The document owner brings the branded Word template (and PowerPoint or Excel, if used) with standard style names: Title, Heading 1–4, Normal, List Bullet.
  • Each SME brings one good past example of the output they want (a past-performance write-up, a staffing shortlist).
  • Pilot users list the tools they work in all day (Claude Desktop, Cursor, Slack, Google Workspace).

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Agenda

Session 1 Session 2

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Running the workshop

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1. Design the assistant

Start with one general assistant. Add a second only when a group of users needs different tools, instructions or models. Ask the room:
  • “Who opens this assistant, and what do they ask first?” This becomes the Subtitle and Welcome Message.
  • “What words do you use that the data does not?” List each term and what it means in the graph. These become Cypher Instructions and Resolve Entities Instructions.
  • “Does it need anything beyond the graph, such as documents or external tools?” This decides the Tool Groups.
  • “Must a user give context before an answer makes sense?” If so, use the Intent Resolver Prompt Extension so the assistant asks before it retrieves. Keep the list short.
Model choice per step lives on the Model Configuration tab. A common pattern is a smaller, fast model for the router, template matcher, Cypher generation and entity resolution, and a stronger model for the report writer. The router and template matcher models must support function calling.

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2. Write the AI Instructions

AI Instructions apply in layers: System > Global > Client > Assistant. Ask: “Is this true for every assistant, or only this one?”
  • Global: firm name, tone, citation rules, what not to answer.
  • Client: rules for one client organization, where the deployment serves several.
  • Assistant: rules that only make sense for one assistant’s audience.
Keep each instruction to one directive. It is easier to switch off one instruction than to edit a long one.

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3. Choose document templates

Ask: “Which outputs do you copy into Word or PowerPoint today?” For each one, agree the file type, the category, what data to retrieve and how to lay it out. DOCX templates carry Word styles and headers; PPTX templates need named slide layouts including blank; XLSX templates are filled in place by an agent flow.

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4. Design each agent flow on paper first

Take one agent candidate card at a time. Before anyone opens the canvas, fill in these six boxes with the SME: A flow is worth building when the work has fixed steps, a review, or a file output. If a single chat question plus a document template does the job, skip the flow.
Agent flows run from the canvas, the API or chat. They do not run on a schedule. Scheduled data jobs are Flows under Process > Flows, which is a different feature.

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5. Personas (optional)

Personas describe who is asking (for example “Partner” or “Analyst”) and can gate a conversation until the user supplies context. They are off by default. Use them only when the same assistant must answer differently for clearly different audiences.

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6. Users in other tools

Ask: “Where do people want to ask questions without opening Experio?” Each user can create a personal API key for the MCP server and use Experio from Claude Desktop, Cursor or VS Code. Users connect Google Workspace and Slack for themselves under Integrations.

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Worked example: Northbridge Consulting

Assistant: “Northbridge Knowledge” AI Instructions
  • Global: “Refer to the firm as Northbridge Consulting. Cite a project code whenever you name a project.”
  • Assistant (Northbridge Knowledge): “When listing people, include their practice and title. Dates in the format 12 March 2025.”
Document template: “Past Performance (DOCX)”, category Case Study, answering Q2.
  • Retrieval instructions: “Find all projects for the named client: project name, project code, dates, service line, description, engagement lead, and the governing SOW.”
  • Output instructions: “One section per project: Client Need, Our Approach, Results, Team. Past tense. No more than 250 words per project.”
Agent flow: “Staffing shortlist” (UC2, owner Sam Whitfield) Paper design: triggered from chat or the Launch a flow menu; inputs role, skills and client; the graph step answers a Q4/Q5-style question; an LLM ranks candidates with reasons; Sam reviews and edits; the output is a DOCX. The flow’s description, which the chat agent reads to decide when to offer it: “Builds a ranked shortlist of Northbridge consultants for a role, given required skills and a client, for the Resource Manager to review.” The Sub-agent toggle is on.

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Accelerate with AI

The Admin Copilot (⌘J or Ctrl+J) is docked on the Agent Flows list and editor. Paste the paper design as plain text, for example: “Build a flow that takes a role, skills and a client, queries the graph for matching employees, ranks the top five, has someone review it, and produces a Word document.” The copilot drafts the flow as a proposal card, checked on the server. It proposes only: you apply the card, save, validate and publish. It can also explain a failed run and propose a targeted fix. See Admin Copilot.

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Entering it in Experio

1

Assistant

AI & Agents > Agent Configuration > Add Assistant. Fill the Basic, Configuration, Model Configuration, Custom Tool Configuration and Agent Configuration tabs. Details in Assistants.
2

AI Instructions

AI & Agents > AI Instructions > Create New. Set scope and order. See AI Instructions.
3

Document templates

Check or add the category in Model & Define > Template Categories, then Model & Define > Document Templates > Add Template: name, type, category, retrieval and output instructions, file. Users pick it from the wand button in chat. See Document Templates.
4

Agent flow

AI & Agents > Agent Flows > New flow, or start from a template (RFP Response Pipeline, Knowledge Graph Report, RFQ Generation). Drag blocks, connect them, bind inputs, then Validate and Publish. Bind the DOCX step to the Human review content output, not to the draft, so a reviewer’s edits reach the file. Turn on Sub-agent and write the description. See Agent Flows and the Flow Blocks Reference.
5

Review queue

Reviewers act in the chat’s flow-run panel, or staff use AI & Agents > Agent Inbox.
6

Personas, MCP, Integrations (optional)

Personas: turn on PERSONAS_ENABLED in Administer > System Settings, then create them in AI & Agents > Personas (Personas). MCP: each user opens their avatar menu > MCP Server and generates a key (MCP Server). For flows that call external tools, register servers in AI & Agents > MCP Servers.

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Common pitfalls

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Exit criteria

  • Every golden question is assigned to chat, a document template, or an agent flow
  • Assistant configured; vocabulary written into Cypher and Resolve Entities Instructions
  • AI Instructions entered at the right scope and reviewed by the super user
  • Each document template generates a correct, branded file from a golden question
  • Each agent flow has a signed-off paper design, is published, has run on a real input, and its reviewer has approved one output
  • Sub-agent flows have descriptions and launch from chat
  • Pilot users know how to use MCP and Integrations, if in scope
  • Golden questions re-scored with the final assistant

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Next

Run user acceptance with the pilot users, re-scoring the golden questions as described in Accuracy validation. Then continue to Go-live. Copy-ready worksheets are on Templates & Worksheets.