The Difference
| Concept | Question it answers | Examples |
|---|---|---|
| Context | What is happening in this conversation? | Customer message, prior conversation history, ticket metadata, source channel, tool results, workflow outputs |
| Knowledge | What does our company know about this topic? | Product docs, policies, troubleshooting steps, FAQs, internal procedures, approved answers |
| Memory | What has Duckie already learned or done in this ongoing ticket? | The order number the customer provided, a prior lookup result, a question Duckie already asked |
| Agent Tables | What structured working set should an autonomous agent query or update? | Per-ticket checklist rows, run scratchpads, workspace queues, agent-specific review lists |
Context: What Duckie Knows Right Now
Context is the situation around the current run. It can include:- The customer’s latest message
- Prior conversation history
- The source channel and ticket metadata
- Customer or account details from connected systems
- Tool results
- Workflow outputs
- Handoff details from a parent run or sub-agent
Knowledge: What Duckie Can Look Up
Knowledge is reusable information Duckie can search or read before answering. Use knowledge for:- Product facts
- Help center articles
- Refund, billing, security, and account policies
- Troubleshooting steps
- Common questions
- Internal procedures
- Approved answers
Memory: What Carries Forward
Memory is continuity within an ongoing conversation or ticket. For autonomous agents, Duckie can use prior conversation context and saved run memory to continue work across turns on the same ticket. Example: if Duckie already asked for an order number and the customer replies with it later, memory helps Duckie continue instead of starting over.Agent Tables: Structured Working Memory
Agent Tables are for structured rows that an autonomous agent should query or update during a run. Use Agent Tables when the agent needs a scoped table-like working set: a checklist, queue, candidate list, extracted facts table, or status tracker. Use ordinary memory when the agent only needs conversational continuity.How They Work Together
For a failed payment question:- Context tells Duckie the customer’s plan, billing ticket, and latest message.
- Knowledge gives Duckie the payment retry policy.
- A tool lookup returns the current invoice status.
- Memory helps Duckie remember that it already checked the invoice when the customer follows up.
- An Agent Table can track structured follow-up rows if the process needs a checklist or review queue.
Common Mistakes
- Do not put permanent policy only in agent instructions. Put it in knowledge.
- Do not expect memory to replace clear knowledge articles.
- Do not pass every detail to a sub-agent. Pass only the Conversation history or Additional context it needs.
- Do not rely on customer-provided identifiers alone for account actions. Use verified context and scoped tools.
Where to Manage Each
| Area | Where to work |
|---|---|
| Context | Analyze > Runs, workflow inputs, source metadata, tool outputs, custom tool Context Variables |
| Knowledge | Train > Knowledge, Knowledge Tags, Knowledge Gaps, connected sources, Custom Knowledge |
| Memory | Multi-turn testing, Test > Playground, Test > Replay Chats, Test > Batch Test, Analyze > Runs |
| Agent Tables | Build > Tables and the Tables section in autonomous agent configuration |
Related Docs
Knowledge
Manage the information agents can search.
Agent Configuration
Scope knowledge, runbooks, workflows, tools, and callable agents.
Agent Tables
Give autonomous agents scoped structured working memory.
Sub-Agents
Pass Conversation history and Additional context to child agents.
Runs
Inspect context, steps, tool calls, and outcomes.