Reasoning Pattern Adaptation

The Reasoning Pattern Adaptation use case enables agents to align with a user’s unique cognitive and decision-making style — powered by Fastino’s personalization embeddings and user-specific summaries.

Overview

Fastino builds an evolving model of how each user reasons, decides, and explains their thinking.
By analyzing language, structure, and decision outcomes across ingested data, it helps agents adapt their reasoning chain to match user expectations.

Key outcomes include:

• More natural explanations and thought sequences.

• Higher trust through familiar reasoning styles.

• Better alignment in multi-turn problem solving.

Core Dimensions of Reasoning Adaptation

Fastino learns these axes over time from user text, corrections, and event outcomes.

Example: Inferring Reasoning Style from User Data

Agents can feed user emails, chat transcripts, or notes into Fastino’s ingestion API:

POST /ingest
{
  "user_id": "usr_42af7c",
  "source": "slack",
  "documents": [
    {
      "doc_id": "doc_reason_001",
      "kind": "conversation",
      "title": "Design Discussion",
      "content": "Let's test hypotheses quickly before building prototypes. Data over opinion."
    }
  ]
}

When queried later:

POST /query
{
  "user_id": "usr_42af7c",
  "question": "How does Ash prefer to reason or make decisions?"
}

Response

{
  "answer": "Ash prefers data-driven, hypothesis-testing reasoning with concise explanations and minimal speculation."
}

Example: Adaptive Reasoning Prompts

When generating responses or performing multi-step reasoning, agents can incorporate user reasoning summaries:

Response

{
  "summary": "Ash reasons analytically and values evidence-based conclusions. Prefers direct recommendations with supporting rationale."
}

This can be injected as a system prompt or context variable:

Example: Self-Adaptive Chain of Thought

1. User asks: “Should we invest more in the next sprint or hold resources?”

2. Agent retrieves reasoning style summary.

3. Fastino returns: “Ash prefers risk-balanced but decisive reasoning.”

4. The agent generates reasoning steps like:

5. Final output is short, structured, and confident — consistent with user behavior.

Learning from Feedback

Corrections and outcomes continuously refine reasoning style:

• If the user edits verbose reasoning → inferred preference for concise logic.

• If the user asks “why?” frequently → inferred preference for explanation depth.

• If the user cancels over-confident decisions → inferred preference for cautious reasoning.

Example feedback ingestion:

POST /ingest
{
  "user_id": "usr_42af7c",
  "source": "assistant_feedback",
  "events": [
    {
      "event_id": "evt_reason_feedback_01",
      "type": "correction",
      "timestamp": "2025-10-27T18:10:00Z",
      "content": "User requested deeper reasoning and more context in decision reports."
    }
  ]
}

Integration in Agent Architectures

Example Implementation (Python)

import requests

BASE_URL = "https://api.fastino.ai"
HEADERS = {"Authorization": "x-api-key sk_live_456", "Content-Type": "application/json"}

def get_reasoning_summary(user_id):
    url = f"{BASE_URL}/summary?user_id={user_id}&purpose=reasoning-style"
    r = requests.get(url, headers=HEADERS)
    return r.json()["summary"]

def adapt_prompt_for_user(user_id, base_prompt):
    summary = get_reasoning_summary(user_id)
    return f"{base_prompt}\n\nUser Reasoning Style: {summary}"

# Example usage
prompt = adapt_prompt_for_user("usr_42af7c", "Analyze recent feature adoption metrics.")
print(prompt)

Advanced Pattern: Reasoning Embeddings

Beyond summaries, developers can store and retrieve reasoning embeddings that represent a user’s cognitive fingerprint.
These can be used for clustering users with similar reasoning patterns or fine-tuning behavior models at scale.

POST /chunks
{
  "user_id": "usr_42af7c",
  "conversation": [
    {"role": "user", "content": "Why should we expand to Europe now?"}
  ],
  "top_k": 3
}

This retrieves context snippets that reflect how the user typically reasons about strategic or analytical topics.

Use Cases

Best Practices

• Use purpose=reasoning-style when fetching summaries for reasoning adaptation.

• Keep summaries ≤1000 characters for embedding efficiency.

• Pair reasoning style with Decision Prediction for deeper personalization.

• Regularly ingest user feedback on output format and reasoning depth.

• For high-stakes tasks, log reasoning traces (kind=decision_record) for transparency.

• Avoid hardcoding cognitive styles — let Fastino infer and evolve them.

Example: Deterministic Reasoning Summary

Response

{
  "summary": "Ash reasons analytically, prefers structured arguments, moderate risk, and concise justifications supported by data."
}

This summary can be embedded into every agent session to maintain continuity and predictability.

Summary

The Reasoning Pattern Adaptation use case allows your agents to think like their users.
By learning each user’s reasoning structure, risk tolerance, and communication style, Fastino enables adaptive, human-aligned cognition across all intelligent systems.

Next, continue to Personalization Use Cases → Routine Prediction to explore how Fastino models behavioral rhythms and predicts daily activity patterns.