Reflect: How Atulya Reasons with Disposition
When you call reflect(), Atulya doesn't just retrieve facts — it reasons about them through the lens of the bank's unique disposition, forming new opinions and generating contextual responses.
Why Reflect?
Most AI systems can retrieve facts, but they can't reason about them in a consistent way. Every response is generated fresh without a stable perspective or evolving beliefs.
The Problem
Without reflect:
- No consistent character: "Should we adopt remote work?" gets a different answer each time based on the LLM's randomness
- No opinion formation: The system never develops beliefs based on accumulated evidence
- No reasoning context: Responses don't reflect what the bank has learned or its perspective
- Generic responses: Every AI sounds the same — no disposition, no point of view
The Value
With reflect:
- Consistent character: A bank configured as "detail-oriented, cautious" will consistently emphasize risks and thorough planning
- Evolving opinions: As the bank learns more about a topic, its opinions strengthen, weaken, or change — just like a real expert
- Contextual reasoning: Responses reflect the bank's accumulated knowledge and perspective: "Based on what I know about your team's remote work success..."
- Differentiated behavior: Customer support bots sound diplomatic, code reviewers sound direct, creative assistants sound open-minded
When to Use Reflect
Use recall() when... | Use reflect() when... |
|---|---|
| You need raw facts | You need reasoned interpretation |
| You're building your own reasoning | You want disposition-consistent responses |
| You need maximum control | You want the bank to "think" for itself |
| Simple fact lookup | Forming recommendations or opinions |
Example:
recall("Alice")→ Returns all Alice factsreflect("Should we hire Alice?")→ Reasons about Alice's fit based on accumulated knowledge, weighs evidence, forms opinion
Disposition Traits
When you create a memory bank, you can configure its disposition using three traits. These traits influence how the bank interprets information and forms opinions during reflect():
| Trait | Scale | Low (1) | High (5) |
|---|---|---|---|
| Skepticism | 1-5 | Trusting, accepts information at face value | Skeptical, questions and doubts claims |
| Literalism | 1-5 | Flexible interpretation, reads between the lines | Literal interpretation, takes things at face value |
| Empathy | 1-5 | Detached, focuses on facts | Empathetic, considers emotional context |
Background: Natural Language Identity
Beyond numeric traits, you can provide a natural language background that describes the bank's identity:
client.create_bank(
bank_id="my-bank",
background="I am a senior software architect with 15 years of distributed "
"systems experience. I prefer simplicity over cutting-edge technology.",
disposition={
"skepticism": 4, # Questions new technologies
"literalism": 4, # Focuses on concrete specs
"empathy": 2 # Prioritizes technical facts
}
)
The background provides context that shapes how disposition traits are applied:
- "I prefer simplicity" + high skepticism → questions complex solutions
- "15 years experience" → responses reference this expertise
- First-person perspective → creates consistent voice
Opinion Formation
When reflect() encounters a question that warrants forming an opinion, disposition shapes the response.
Same Facts, Different Opinions
Two banks with different dispositions, given identical facts about remote work:
Bank A (low skepticism, high empathy):
"Remote work enables flexibility and work-life balance. The team seems happier and more productive when they can choose their environment."
Bank B (high skepticism, low empathy):
"Remote work claims need verification. What are the actual productivity metrics? The anecdotal benefits may not translate to measurable outcomes."
Same facts → Different conclusions because disposition shapes interpretation.
Opinion Evolution
Opinions aren't static — they evolve as new evidence arrives. Here's a real-world example with a database library:
| Event | What the bank learns | Opinion formed |
|---|---|---|
| Day 1 | "Redis is open source under BSD license" | "Redis is excellent for caching — fast, reliable, and OSS-friendly" (confidence: 0.85) |
| Day 2 | "Redis has great community support and documentation" | Opinion reinforced (confidence: 0.90) |
| Day 30 | "Redis changed license to SSPL, restricting cloud usage" | "Redis is still technically strong, but license concerns for cloud deployments" (confidence: 0.65) |
| Day 45 | "Valkey forked Redis under BSD license with Linux Foundation backing" | "Consider Valkey for new projects requiring true OSS; Redis for existing deployments" (confidence: 0.80) |
Before the license change:
"Should we use Redis for our caching layer?" → "Yes, Redis is the industry standard — fast, battle-tested, and fully open source."
After the license change:
"Should we use Redis for our caching layer?" → "It depends. For cloud deployments, consider Valkey (the BSD-licensed fork). For on-premise, Redis remains excellent technically."
This continuous learning ensures recommendations stay current with real-world changes.
Disposition Presets by Use Case
Different use cases benefit from different disposition configurations:
| Use Case | Recommended Traits | Why |
|---|---|---|
| Customer Support | skepticism: 2, literalism: 2, empathy: 5 | Trusting, flexible, understanding |
| Code Review | skepticism: 4, literalism: 5, empathy: 2 | Questions assumptions, precise, direct |
| Legal Analysis | skepticism: 5, literalism: 5, empathy: 2 | Highly skeptical, exact interpretation |
| Therapist/Coach | skepticism: 2, literalism: 2, empathy: 5 | Supportive, reads between lines |
| Research Assistant | skepticism: 4, literalism: 3, empathy: 3 | Questions claims, balanced interpretation |
What You Get from Reflect
When you call reflect():
Returns:
- Response text — Disposition-influenced answer
- Based on — Which memories were used (with relevance scores)
Example:
{
"text": "Based on Alice's ML expertise and her work at Google, she'd be an excellent fit for the research team lead position...",
"based_on": {
"world": [
{"text": "Alice works at Google...", "weight": 0.95},
{"text": "Alice specializes in ML...", "weight": 0.88}
]
}
}
Note: New opinions are formed asynchronously in the background. They'll influence future reflect() calls but aren't returned directly.
Why Disposition Matters
Without disposition, all AI assistants sound the same. With disposition:
- Customer support bots can be diplomatic and empathetic
- Code review assistants can be direct and thorough
- Creative assistants can be open to unconventional ideas
- Risk analysts can be appropriately cautious
Disposition creates consistent character across conversations while allowing opinions to evolve with evidence.
Next Steps
- Retain — How rich facts are stored
- Recall — How multi-strategy search works
- Reflect API — Code examples, parameters, and tag filtering