Using Memory

Purpose

Memory transforms agents from stateless tools into learning entities. Instead of starting fresh each run, agents can:

• Recall what worked before
• Avoid repeating mistakes
• Apply organization-specific knowledge
• Build expertise over time

When to Search

At the start of every workflow - proactively recall relevant context before taking action.

Search when:

• Beginning a new analysis
• Before creating discoveries or insights
• When context from past runs would help
• When working with familiar entities (tables, metrics, users)

When to Create

Only after something valuable happens - quality over quantity.

Create a memory when:

• A discovery was approved or rejected (learn from feedback)
• You found an organization-specific pattern or threshold
• A technique worked well (or failed in a specific context)
• You learned something that would help future runs

Do not create memories for:

• Routine success ("query returned results")
• Minor details ("table has 1M rows")
• Information easily re-discoverable
• Duplicates of existing memories

Memory Types

Episodic - What Happened

Record specific events with outcomes.

Use when: Something happened that you should remember

• Discovery was created, approved, or rejected
• Analysis succeeded or failed
• User gave feedback

Include: The outcome (success, failure, rejected, approved)

Example: "Created DSC-789 for 15% revenue drop. User rejected - said threshold should be 20%."

Semantic - What You Know

Record facts and knowledge independent of specific events.

Use when: You learned a fact or pattern

• Organization prefers certain thresholds
• Data has specific characteristics
• Business rules or preferences

Include: Confidence level if uncertain

Example: "Organization ignores metric changes under 20% - confirmed by user feedback."

Procedural - How To Do Things

Record techniques and approaches that work.

Use when: You discovered a technique

• Query pattern that performs well
• Workaround for a limitation
• Best practice for a specific situation

Include: Success rate and failure contexts

Example: "For sales table: use date_trunc with timezone filter for accurate daily aggregations. Fails without timezone on UTC data."

Decision Tree

1. Did something HAPPEN? (event with outcome) → Episodic

2. Did you LEARN A FACT? (pattern, threshold, preference) → Semantic

3. Did you DISCOVER A TECHNIQUE? (approach, workaround) → Procedural

4. None of the above? → Probably not worth saving

Decision Matrix

Quick reference for common situations:

Situation	Type	Scope	Importance	Decay
Discovery approved	Episodic	Workflow	8	Weekly
Discovery rejected with feedback	Episodic	Workflow	9	Monthly
User stated preference	Semantic	Organization	9	Monthly
Data pattern found	Semantic	Organization	7	Weekly
Query optimization worked	Procedural	Organization	8	Monthly
Workaround for edge case	Procedural	Workflow	6	Weekly
Entity-specific behavior	Semantic	Entity	7	Weekly
Workflow failed for specific reason	Episodic	Workflow	8	Weekly
Business rule confirmed	Semantic	Organization	9	Monthly
Technique failed in context	Procedural	Workflow	7	Weekly

Memory Scopes

Organization

Knowledge that applies to everyone in the org.

• Business rules and thresholds
• Company-wide preferences
• Domain terminology

Workflow

Context specific to this workflow type.

• Patterns for this analysis type
• Workflow-specific learnings

Agent

Knowledge private to a specific agent.

• Agent-specific optimizations
• Personal learnings
• Specialized expertise

Entity

Facts about a specific entity (insight, discovery, table).

• Entity-specific preferences
• Historical context for that entity

Scope Decision Tree

1. Does this apply to the entire organization? → Organization

2. Is this specific to this workflow type? → Workflow

3. Is this about a specific entity (INS-, DSC-)? → Entity

4. Is this my personal learning/optimization? → Agent

Source Linking

Link memories to specific entities when relevant:

• Insights: INS-123
• Discoveries: DSC-456
• Segments: SGM-789
• Dashboards: DSH-101

This enables searching for all memories related to a specific entity.

Importance

Rate importance honestly - it affects how long memories persist.

Score	Meaning	Examples
9-10	Critical	Major outage cause, critical business rule
7-8	Valuable	Clear pattern, confirmed preference
5-6	Moderate	Potentially useful, unconfirmed
1-4	Low	Minor detail, easily rediscovered

Guideline: Only save memories with importance >= 7. Lower importance creates noise.

Forgetting Curve

Memories decay over time without reinforcement.

• Searching reinforces memories (they stay relevant longer)
• Higher importance = slower decay
• Frequent access = slower decay

Decay rates:

• Daily - Session context, temporary learnings
• Weekly - Short-term patterns (default)
• Monthly - Core knowledge, stable facts

Consolidation

Over time, the system automatically:

Episodic → Semantic: When multiple similar events occur, they get abstracted into a general pattern.

• 3+ similar episodic memories → 1 semantic memory
• Example: "Revenue drop triggered discovery" (x3) → "Revenue anomalies consistently trigger discoveries"

Procedural merging: Similar techniques get consolidated into best practices.

• Preserves success rates and failure contexts

Scope promotion: Patterns seen across multiple workflows get promoted to organization scope.

• Workflow patterns that repeat → Organization-level knowledge

Writing Effective Content

Good memories are specific, actionable, and include context.

Good vs Bad Examples

Bad	Good
"Revenue issue"	"Revenue dropped 15% in Q4 due to seasonal churn"
"User didn't like it"	"User rejected DSC-123 - said 10% threshold is too low, prefers 20%"
"Query was slow"	"Sales table aggregation: use date_trunc with index, reduced from 30s to 2s"
"Something about funnels"	"Funnel analysis requires strict event ordering for accurate conversion rates"

Content Checklist

• What happened or what did you learn? (the core fact)
• Why does it matter? (impact or implication)
• What should change? (actionable guidance)
• Any constraints? (when this applies or doesn't)

Best Practices

Searching

• Search broadly at workflow start
• Use natural language queries
• Filter by type when you know what you need
• Search parent scopes to find org-level knowledge

Creating

• Be specific, not vague
• Include context that makes the memory actionable
• Set importance honestly
• Add relevant tags for discoverability
• Link to entities when applicable

Quality Over Quantity

• One high-quality memory > many low-quality ones
• If unsure whether to save, don't
• Duplicate memories create noise
• Let unimportant memories decay naturally

Common Pitfalls

• Not searching at workflow start (missing valuable context)
• Saving everything (creates noise, reduces signal)
• Vague content ("something happened with revenue")
• Wrong type selection (fact stored as event)
• Importance inflation (everything marked as 9-10)
• Missing outcome for episodic memories
• Missing success_rate for procedural memories

Troubleshooting

Search returns no results:

• Broaden query terms (use synonyms, related concepts)
• Search parent scopes (workflow → organization)
• Check if memories existed but decayed (low importance + no recent access)

Search returns low relevance:

• Refine query to be more specific
• Filter by memory type if you know what you need
• Results may have decayed - consider if re-learning is needed

Expected memory is missing:

• May have decayed due to low importance or infrequent access
• Check if it was saved at wrong scope (entity vs workflow)
• Re-create if the knowledge is still valuable

Too much noise in results:

• Be more selective when creating (importance >= 7)
• Let low-value memories decay naturally
• Use more specific search queries

Reference Files

• Memory types - Read when choosing between episodic, semantic, and procedural memory types
• Memory scopes - Read when deciding the right scope (organization, workflow, agent, entity, user)
• Forgetting curve - Read when tuning decay rates or understanding why memories disappeared