KB prompts vs. agents

Knowledge-base prompts and agents solve different problems. A KB prompt defines shared answering behavior for a knowledge base. An agent packages a repeatable job, model, tools, skills, delegates, and optional schedule on top of one knowledge base.

The fast decision

You need to…	Use
Give every chat on a KB the same voice, boundaries, or formatting	KB Persona + Instructions
Fully replace Quick or Deep answering behavior	A mode-specific full Custom Prompt
Save a repeatable goal with a fixed model and capabilities	Agent
Use connected apps, custom tools, MCP servers, skills, or delegation consistently	Agent
Run work through the API or on a schedule and inspect run history	Agent
Give several specialized agents one shared answering policy and corpus	KB prompts + agents

Start with Persona + Instructions on the knowledge base. Create an agent when you need repeatable execution, capabilities, versioned deploys, or run history.

What belongs in KB prompts

Use KB prompts for rules that should follow the knowledge base everywhere:

Persona — who the assistant is and how it presents itself.
Instructions — shared rules, boundaries, citation style, and answer format.
Quick Custom Prompt — a complete replacement for Quick-mode answering behavior.
Main/Deep Custom Prompt — a complete replacement for Deep-mode answering behavior.
SQL and chart instructions — shared behavior for connected data.

A full Custom Prompt takes over for its mode and supersedes Persona and Instructions. Prefer Persona + Instructions unless you need complete white-label control, and put the whole answering contract inside a full Custom Prompt.

What belongs in an agent

Use an agent for a named job such as “triage support tickets” or “prepare a weekly account brief.” The agent owns:

Goal and job-specific instructions
Model and knowledge base selection
Web search and durable-memory toggles
Connected apps, custom tools, MCP servers, and skills
Callable agents for delegation
Schedule, versions, runs, and run history

The knowledge base is fixed when the agent is created. Each configuration deploy creates a numbered version; name and status edits do not.

The common pattern: use both

Knowledge base
  Shared Persona + Instructions
  Private documents and connected data
    ├── Support agent     Goal: resolve customer questions
    ├── QA agent          Goal: find documentation gaps
    └── Weekly brief      Goal: summarize trends on a schedule

The KB supplies the shared corpus and answering contract. Each agent adds a focused goal and only the capabilities required for its job.

Configure it in the dashboard

Set the shared KB behavior

Open the knowledge base and set Persona and Instructions. Test them in a normal chat first.

Create the specialized agent

Choose the same knowledge base, then add a specific goal, job instructions, model, and capabilities.

Run before scheduling

Test the agent interactively, inspect its run and sources, then enable a schedule only when the result is dependable.

Build it with the API

Update shared KB behavior:

curl -X PATCH "$AUTOSAGE_URL/knowledge-bases/KB_ID" \
  -H "Authorization: Bearer $AUTOSAGE_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "persona": "You are Acme Support.",
    "instructions": "Answer from approved sources, cite them, and say when the KB does not contain the answer."
  }'

Then create a focused agent on that KB:

curl -X POST "$AUTOSAGE_URL/agents" \
  -H "Authorization: Bearer $AUTOSAGE_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "tenant_id": "TENANT_ID",
    "name": "Support specialist",
    "goal": "Resolve customer support questions.",
    "instructions": "Ask one clarifying question when account context is missing.",
    "model_id": "MODEL_ID",
    "selected_knowledge_base_id": "KB_ID"
  }'

Get started

Core concepts

Lifecycles

Guides

API reference

KB prompts vs. agents

The fast decision

What belongs in KB prompts

What belongs in an agent

The common pattern: use both

Configure it in the dashboard

Build it with the API

Continue the learning path

Chat vs. agent runs

Agent in two minutes

​The fast decision

​What belongs in KB prompts

​What belongs in an agent

​The common pattern: use both

​Configure it in the dashboard

​Build it with the API

​Continue the learning path

Chat vs. agent runs

Agent in two minutes

The fast decision

What belongs in KB prompts

What belongs in an agent

The common pattern: use both

Configure it in the dashboard

Build it with the API

Continue the learning path