Chitin.ai

Indestructible agentic memory for autonomous infrastructure remediation using CockroachDB Distributed Vector Search, Managed MCP, and AWS Bedrock.

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posted an update —

** Chitin.ai — Project Log & Progress Update**

Here is a breakdown of how Chitin.ai has evolved from initial architecture design to a fully deployed, fault-tolerant autonomous agent engine.

Development & Feature Timeline

  • Core Agent & Database Setup:
  • Configured the base FastAPI, Next.js, and Streamlit execution environments.

  • Integrated CockroachDB Cloud as the primary distributed database tier for ACID-compliant state storage.

  • Vector Memory Integration:

  • Enabled pgvector alongside AWS Bedrock Titan embeddings to index telemetry logs and match historical outage playbooks via cosine similarity search.

  • Chaos Mode & Fault Tolerance:

  • Built and tested automated checkpointing. Injected mid-pipeline process terminations to confirm state recovery in under 1.2 seconds.

  • Streamlit Cloud Deployment:

  • Successfully deployed the interactive control dashboard to Streamlit Cloud.

Code Snippet: Transactional Checkpoint Model

Here is how Chitin.ai schema defines state persistence with vector support in SQLAlchemy:

from pgvector.sqlalchemy import Vector
from sqlalchemy import Column, Integer, String, JSON, DateTime
from src.database.cockroach import Base
import datetime

class AgentCheckpoint(Base):
    __tablename__ = "agent_checkpoints"

    id = Column(Integer, primary_key=True, index=True)
    session_id = Column(String, nullable=False, index=True)
    execution_step = Column(String, nullable=False)
    state_payload = Column(JSON, nullable=False)
    telemetry_vector = Column(Vector(1536))  # AWS Bedrock Titan Embedding
    created_at = Column(DateTime, default=datetime.datetime.utcnow)

Try the Live App

What features or additional infrastructure integrations would you like to see next? Drop your thoughts in the comments!

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