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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.

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The fast decision

You need to…Use
Give every chat on a KB the same voice, boundaries, or formattingKB Persona + Instructions
Fully replace Quick or Deep answering behaviorA mode-specific full Custom Prompt
Save a repeatable goal with a fixed model and capabilitiesAgent
Use connected apps, custom tools, MCP servers, skills, or delegation consistentlyAgent
Run work through the API or on a schedule and inspect run historyAgent
Give several specialized agents one shared answering policy and corpusKB prompts + agents
Start with Persona + Instructions on the knowledge base. Create an agent when you need repeatable execution, capabilities, versioned deploys, or run history.

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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.

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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.

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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.

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Configure it in the dashboard

1

Set the shared KB behavior

Open the knowledge base and set Persona and Instructions. Test them in a normal chat first.
2

Create the specialized agent

Choose the same knowledge base, then add a specific goal, job instructions, model, and capabilities.
3

Run before scheduling

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

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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"
  }'

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Continue the learning path

Chat vs. agent runs

Understand what happens when the new agent executes.

Agent in two minutes

Follow the shortest complete setup.