Assistants

Overview

Assistants are the AI personas users interact with in Experio. Each assistant has its own configuration including model assignments, tools, behavior settings, and agent architecture. You can create multiple assistants tailored to different use cases such as data exploration, conflict analysis, or knowledge transition. Navigate to Admin Panel > Settings > Agent Configuration. The interface provides a full-page CRUD experience with a tabbed form for managing all assistant settings. From the list view you can search, create, edit, and delete assistants. Clicking an assistant or the Add Assistant button opens the detail page with the following tabs.

Basic Tab

Field	Description
Title	The assistant’s display name shown to users
Subtitle	A brief description shown on the assistant card
Icon	Select from a dropdown of available icons (e.g., robot, brain, lightbulb, analytics)
Welcome Message	The greeting shown when a user starts a new conversation with this assistant
Display Order	Display order on the Agents page (lower numbers appear first)
Enabled	Whether the assistant is available to users
Staff Only	Restrict this assistant to staff users only

Gating

Gating requires users to provide specific context (for example: company size, industry, strategic focus) before the assistant will process their queries. When enabled, the first message of every conversation is evaluated against the gating prompt; if the required context is missing, the assistant returns a clarification asking for it instead of running the normal pipeline. Once a conversation provides complete context, gating is satisfied for the rest of that conversation.

Field	Description
Enable Gating	Toggle gating on or off for this assistant. When off, no gating evaluation runs and there is no overhead.
Gating Prompt	Free-form text describing what context the user must provide. Shown to users in an amber banner on the welcome page and used by the LLM to decide whether the user’s message is complete. Only visible when Enable Gating is on; clearing the toggle clears the prompt.

Write the gating prompt as a checklist of required information, e.g.:

User must provide:
- Company size and industry
- Strategic objectives
- Key challenges they want to address

Users can amend the context they originally provided at any time by clicking the clipboard icon next to the message input — the panel shows the original context plus any updates.

Configuration Tab

Field	Description
System Prompt	Custom system prompt for this assistant. Leave empty to use the default prompt for the assistant type.
Use Document Context	Include document context from vector search in responses
Allow Per-Question Model Switch	When enabled, users can select a report-writer model per message; for LangGraph deep agents this applies to the report phase only

Model Configuration Tab

Each assistant is assigned models that control how it generates responses. All model dropdowns are populated from the configured Model Configurations.

Field	Description
Reasoning model (default pipeline)	Default for LangGraph scope, retrieval, router, and summarization when no per-step override is set. Uses a Chat model config. Also the chain used when the report-writer model is unset.
Report writer model	Used for the LangGraph report phase (the streamed answer users see), and for final responses in native/planning agents. Same Chat model type as other assistant slots. If unset, falls back to the reasoning model.

For LangGraph deep agents, the reasoning model drives the pipeline; the report writer model is used only for the final report step. Per-question model selection, when enabled, overrides the report writer model for that message.

The Model Configuration tab also includes per-tool model overrides:

Field	Description
Cypher Reasoning Model	Model used for generating Cypher graph database queries. Falls back to the reasoning model if not set.
Resolve Entities Model	Model used for entity resolution in knowledge graph lookups. Falls back to the reasoning model if not set.
Intent Resolver Model	Model used for classifying user intent and routing queries. Falls back to the reasoning model if not set.
Orchestrator Model	Model used for orchestrator checkpoint nodes (think/write_todos). These nodes only plan tasks and show progress to the user — they do not generate the final answer. Use a fast, inexpensive model here. Falls back to the reasoning model if not set.
Router Model	Model used for the router — the first model call of every turn, which decides how the question should be handled. It only classifies the request, so it does not need a large model. Must support function calling. Falls back to the reasoning model if not set.
Template Matcher Model	Model used for the template matcher, which runs immediately after the router on every turn and checks whether the message matches a document template. Like the router it only classifies, so a smaller model is usually enough. Must support function calling. Falls back to the reasoning model if not set.

Use a smaller, faster model for the router, template matcher, Cypher generation, entity resolution, and orchestrator checkpoints to reduce latency and cost, while keeping a more capable model for the final answering step.

The router and the template matcher both run before any progress is shown, one after the other, so the user waits on both before the assistant appears to start. Because each only classifies the message rather than composing an answer, they are usually the safest places to trade model size for speed.

Both of these steps request structured output using function calling, so whichever model you pick must support it. A model that does not will fail the very first step of every conversation, not just the step you assigned it to. The template matcher additionally decides template clarification, so an underpowered model there can send a document request down the wrong path rather than simply costing a few seconds.

Custom Tool Configuration Tab

Custom prompt instructions for specific tools. These are appended to the default prompts.

Field	Description
Cypher Instructions	Custom instructions appended to the Cypher generation prompt
Resolve Entities Instructions	Custom instructions for the entity resolution step

Agent Configuration Tab

Control the agent architecture and capabilities available to the assistant.

Agent Mode

Select one agent mode at a time using the card-based selector. New assistants default to Deep Agent.

Mode	Description
Deep Agent	LangGraph-based 3-phase workflow (scope, retrieval, report) with TODO tracking, file offloading, and subagent delegation
Planning Agent	Multi-turn planning agent that creates a step-by-step plan before execution
Native Agent	Native LLM tool calling (ReAct loop)
None	No agent orchestration

Tool Groups

Select which tool groups the assistant has access to using the pill-based multi-select. Click a pill to toggle it on/off. Available groups include Knowledge Base, Employee Analysis, OCI Analysis, Graph Query, Document Processing, Reference Documents, and more. If no tool groups are selected, defaults are applied based on the assistant type.

Only one agent mode can be active at a time. The interface enforces this automatically. Deep Agent provides the most thorough analysis but takes longer to respond. For faster, simpler interactions, use Native Agent instead.

When Deep Agent is selected, users see a Deep Research toggle in the chat interface. When activated, this enables parallel graph context queries (GCQ) alongside document search during the scope phase, producing more thorough but slower results. This is a per-message toggle — users can enable it for complex queries and disable it for quick lookups.

Deep Agent Settings Tab

When Deep Agent is selected as the agent mode, a Deep Agent Settings tab appears with additional configuration. This tab is hidden when other agent modes are selected.

Architecture Settings

Field	Default	Description
Use LangGraph architecture	`false`	Enable pure LangGraph 3-phase workflow
Enable subagents	`true`	Enable focused subagents for parallel retrieval tasks
Recursion limit	`30`	Max recursion depth for agent execution
Stream recursion limit	`100`	Max streaming iterations before stopping
Enable checkpointer	`true`	Enable state persistence across conversation turns
Enable store	`true`	Enable PostgresStore for agent memory and context sharing
Checkpoint durability	`exit`	Controls checkpoint frequency (`exit` saves on completion)
Enable orchestrator	`true`	Enable orchestrator checkpoint nodes that provide user-visible progress updates (thinking steps, task tracking). When disabled, these nodes become pass-through — the pipeline still runs but skips the orchestrator LLM calls, reducing latency and cost.

Disabling the orchestrator does not change the agent’s retrieval or answer quality. It only removes the intermediate “thinking” and “task planning” steps that are shown to the user during processing. This is useful for simpler queries where the overhead of orchestrator LLM calls is not needed.

Retrieval Settings

Field	Default	Description
Document search K	`3`	Number of similar documents to retrieve per search
Document search score threshold	`0.7`	Minimum similarity score for document results (0.0 to 1.0)

Scope Settings

Field	Default	Description
Scope max count threshold	`1000`	Max results before the agent asks the user to narrow their query
Min path confidence	`0.5`	Minimum confidence score for intent resolver routing paths
Use resolved intent	`true`	Use the router-rephrased intent for all downstream nodes instead of the raw user message

Clarifying Questions

When the deep agent needs information before it can answer, it asks with a question card instead of writing the question into its reply: a few questions, each with plain-language options the user can multi-select or extend with their own answer. The user’s answers arrive as the next message and the agent resumes — re-running retrieval when the answers change what must be fetched, or only rewriting the answer when they change how it is presented. Follow-up suggestions and filters are suppressed while a question is open. See Questions from the Assistant for the user’s side. The agent asks when the request is genuinely ambiguous, or when nothing in the knowledge base matches it — the card then offers the closest topics it did recognize. To make an assistant gather specific information before it answers, put that in the Intent Resolver Prompt Extension on the Deep Agent tab. An extension such as “Before resolving the query, establish which business unit the user belongs to (Retail, Wholesale, Manufacturing) and whether they want a summary or a detailed report” produces a card before retrieval, so the answer is scoped correctly the first time. An AI instruction that demands information still works, but it reaches the report writer rather than the scope phase, so the question is asked after the data has been fetched. How much the agent may ask is configurable per assistant:

Setting	Default	Description
Max clarifying questions	`5`	How many questions may appear on one card before the agent must proceed (1-10)
Max options per question	`6`	How many suggested answers each question offers; the user can always add their own (2-10)
Max clarification rounds	`2`	How many times the agent may come back and ask again before it must answer with what it has (1-5)

An instruction that demands information makes the assistant open with a question on every request that doesn’t already supply it. Keep the list of required details short, and word each one so the agent can turn it into two to six options — an instruction that asks for something open-ended (“find out the user’s objectives”) produces a card with nothing to pick from, only a free-text box.

The agent will not interrogate a user indefinitely: after two answered rounds of questions for the same request it stops asking, answers with what it has, and explains what it could not determine.

Report Settings

Field	Default	Description
Report data preview limit	`300`	Max rows of retrieved data included in the report writer prompt
Report max data tokens	`50000`	Max tokens allocated for retrieved data in the report context. Rows are dropped from the end until the content fits within this budget, and the report writer is then told it is seeing a partial sample and must say the results were truncated rather than state totals or completeness. If not even one row fits after an aggressive per-field retry, the writer is told the retrieved data could not be included at all and must not describe or count it.
Report writer fallback models	(none)	Fallback models for the report writer phase. On 429 rate-limit errors, these models are tried in order.

Configure fallback models from different providers (e.g., primary: Azure GPT-5.1, fallback: Google Gemini) to ensure the report phase can complete even when one provider hits rate limits. The primary answering model is always tried first.

Graph Context Settings

Field	Default	Description
Auto-include graph context if under	`30000`	Token threshold for auto-including graph context in the report. If the total graph context is under this limit, it is included automatically without requiring a store lookup.
Per-entity auto-include threshold	`20000`	Max tokens per individual entity for selective packing. Entities exceeding this limit are summarized instead of included in full.

Entities that fit these thresholds are delivered directly in the report context; the rest are held in the agent store and listed for the report writer to retrieve on demand. If an entity’s store write fails or is never attempted (for example when Enable store is off), it is withdrawn from that list and the report writer is instructed to state in the answer that part of the graph context was unavailable, rather than present ungrounded relationship claims.

LangGraph Prompt Extensions

The Deep Agent Settings tab also includes a LangGraph Prompt Extensions section. These are custom instructions appended to each LangGraph node prompt, allowing fine-tuning of agent behavior at each stage of the pipeline without code changes.

Prompt Extension	Applied To
Retrieval	The retrieval node that searches documents and knowledge graph
Report Writer	The final report generation node that synthesizes findings
Intent Resolver	The router that classifies user intent and selects the execution path
Orchestrator Pre-Scope	The orchestrator before the scoping phase begins
Orchestrator Pre-Retrieval	The orchestrator before the retrieval phase begins
Orchestrator Pre-Report	The orchestrator before the report generation phase begins
Router	The initial routing node that determines the query type
Retrieval Fallback	The fallback retrieval strategy when primary retrieval returns insufficient results
Summarization	The summarization node used for condensing large result sets

Use prompt extensions to add domain-specific instructions like “Always cite contract numbers” or “Format financial data as tables” without modifying the underlying agent code.

Agent Flows

Assistants are no longer wired to a single Agent Flow. Instead, a flow opts into chat by turning on its Sub-agent toggle, and the assistant’s chat agent discovers and launches it as a background sub-agent when a user’s request matches the flow’s description. See Agent Flows for how to enable a flow and how launching works.

Getting Started

Data Sources

Knowledge Graph

User Management

Integrations

Settings

Monitoring

Overview

Basic Tab

Gating

Configuration Tab

Model Configuration Tab

Custom Tool Configuration Tab

Agent Configuration Tab

Agent Mode

Tool Groups

Deep Agent Settings Tab

Architecture Settings

Retrieval Settings

Scope Settings

Clarifying Questions

Report Settings

Graph Context Settings

LangGraph Prompt Extensions

Agent Flows

​Overview

​Basic Tab

​Gating

​Configuration Tab

​Model Configuration Tab

​Custom Tool Configuration Tab

​Agent Configuration Tab

​Agent Mode

​Tool Groups

​Deep Agent Settings Tab

​Architecture Settings

​Retrieval Settings

​Scope Settings

​Clarifying Questions

​Report Settings

​Graph Context Settings

​LangGraph Prompt Extensions

​Agent Flows

Overview

Basic Tab

Gating

Configuration Tab

Model Configuration Tab

Custom Tool Configuration Tab

Agent Configuration Tab

Agent Mode

Tool Groups

Deep Agent Settings Tab

Architecture Settings

Retrieval Settings

Scope Settings

Clarifying Questions

Report Settings

Graph Context Settings

LangGraph Prompt Extensions

Agent Flows