Cedron Agent: Building an Enterprise Dual-Role MCP & Agentic RAG Platform

How Cedron Technologies engineered a Rails 8.1 platform that combines 3,072-dimensional vector search, $O(1)$ streaming log parsing, and Model Context Protocol (MCP) tooling to reduce incident MTTR by 86.6%.

86.6%
MTTR Reduction
From 45 mins down to 6 mins
O(1)
Constant RAM Usage
Multi-GB log parser streaming
3,072-D
pgvector Embeddings
High-accuracy semantic RAG
0 ms
UI Server Blocking
100% Async ActionCable streaming

Executive Summary & Background

DevOps, Site Reliability Engineering (SRE), and software engineering teams are routinely inundated with thousands of raw log lines during production incidents. Manually identifying root causes, assembling multi-line stack traces, scrubbing credentials, and opening tracking tickets slows down resolution times significantly.

To solve this, Cedron Technologies designed and built Cedron Agent — a production-ready, full-stack AI platform built on Ruby on Rails 8.1, Google Gemini & OpenAI LLMs, PostgreSQL (`pgvector`), and the Model Context Protocol (MCP).

Core Architecture & Dual-Role MCP

Cedron Agent acts as both an MCP Server and an MCP Client, forming a flexible, open architecture for enterprise tooling:

MCP Server (`FastMCP`)

Exposes internal tools over `/mcp/sse` and `/mcp/messages`. External IDEs (Claude Desktop, Antigravity) can connect and execute tools remotely.

MCP Client (`LlmAgentService`)

Orchestrates LLM tool calls (Gemini/OpenAI), manages memory, executes function calls, and feeds results back into the conversation context.

// config/initializers/fast_mcp.rb FastMcp.mount_in_rails( Rails.application, name: "rails-mcp-server", path_prefix: "/mcp", messages_route: "messages", # Handles POST to /mcp/messages sse_route: "sse" # Handles GET to /mcp/sse )

RAG Pipeline & $O(1)$ Log Streaming

Standard RAG systems fail when loading multi-gigabyte log files into server RAM. Cedron Agent uses a custom Streaming Log Parser (`LogParserService`) that reads files line-by-line using `File.foreach` in $O(1)$ constant memory.

It performs automated multi-line stack trace assembly, MD5 error fingerprint deduplication, and PII credential scrubbing before passing sanitized chunks to PostgreSQL `pgvector` for semantic similarity search.

# Semantic Retrieval via pgvector Cosine Distance query_vector = GeminiEmbedding.generate(user_query) DocumentChunk.nearest_neighbors(:embedding, query_vector, distance: "cosine").first(5)

Performance & Business Impact

Metric Before Cedron Agent With Cedron Agent Impact
Incident MTTR 45 minutes / incident 6 minutes / incident 86.6% Faster
Log Processing RAM Up to 4 GB (O(N) load) < 50 MB (O(1) stream) 98.7% Reduction
Jira Ticket Accuracy Manual (Missing traces) Automated via MCP Tool 100% Formatted
UI Responsiveness Server blocking 0 ms (ActionCable Streams) Non-blocking

Want to build a custom AI Agent or RAG platform?

Cedron Technologies architects enterprise AI Agents, Model Context Protocol (MCP) integrations, and high-scale cloud infrastructure for growing businesses.

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