Blog

From the OLAV team

Release announcements, architecture deep-dives, and notes on building AI-native infrastructure tooling.

· OLAV Team Architecture NetOps AI Agent Log Analysis Root Cause Analysis

OLAV vs. Log-Based AI: Why Network Operations Needs More Than a Smarter grep

Most 'AI for operations' tools are just putting an LLM on top of log search. OLAV takes the opposite approach: model the network engineer's entire workflow — collection, parsing, layered diagnosis, snapshot comparison, simulation, and change planning — as a team of AI specialists. Here's why that matters and how it works.

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· OLAV Team Continual Learning Agent Architecture Local LLM Fine-Tuning Self-Improvement

One Agent, Three Ways to Learn: How OLAV Builds a Self-Improving Agent

Learning isn't one thing. OLAV's agent improves across three layers at three speeds — memory that learns every run, weights it can consolidate into with a benchmark gate, and a harness that tunes its own knobs — unified by one loop and one governance contract so they reinforce instead of fight. Here's the story of how we built it, including the times the system correctly threw its own work away.

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