Insights & Updates
Thoughts on agent infrastructure, production reliability, and building for the next paradigm of software.
The agent observability gap: why monitoring isn't enough
Observability tools like LangSmith and W&B are great for watching agents. But watching isn't managing. Here's why agents need a runtime layer, not just a dashboard.
NVIDIA NIM for agents: what developers need to know
NVIDIA NIM is a game-changer for inference performance. We break down how Anchor integrates NIM for intelligent routing, KV-cache optimization, and real-time GPU utilization tracking.
Persistent sessions: how Anchor prevents context loss
When an agent crashes mid-task, you lose everything — messages, tool outputs, model responses. Anchor's Redis Streams-backed sessions persist the full context so your agent resumes exactly where it left off.
Replay at zero cost: debugging AI agents like a human
Anchor's replay engine lets you re-execute any past agent run step by step. Swap the model, test alternatives — against real historical data. Stored tool responses replay at zero cost.
Hybrid routing: the right model at the right time
Not every step in an agent run needs GPT-4o. Anchor routes simple steps to cheap public models and complex reasoning or sensitive data to NVIDIA NIM — reducing cost without sacrificing quality.