Notes from the Vektor team
Engineering posts on agent evaluation, tracing, memory, and the messy reality of shipping AI to production users.
Agent infrastructure blog
A Vektor blog series on routing, tracing, semantic caching, tool governance, evaluation gates, prompt releases, and memory evaluation for production agent systems.
Routing Production Agent Traffic
Route agent traffic by intent, cost, latency, region, and failure state, then capture the final provider and model decision inside the Vektor trace.
Full-Span Tracing for AI Agents
Use Vektor traces to debug the full chain: user input, planner decisions, tool calls, retrieved memory, model outputs, retries, and final evaluation scores.
Semantic Caching Controls
Reduce repeated LLM calls while preserving freshness, tenant boundaries, prompt-version boundaries, and Vektor evaluation checks.
Tool Governance for Agent Workflows
Give agents access to external tools without giving every prompt unrestricted authority. Vektor traces every tool request, approval decision, and result.
Evaluation Quality Gates
Ship Vektor-tracked AI changes through quality gates that combine deterministic validation, judge-model scoring, golden datasets, and live regression monitoring.
Prompt Release Management
Treat prompts like deployable artifacts. Version the prompt, model, tools, retrieval policy, and Vektor evaluator suite together.
Memory and Retrieval Evaluations
Evaluate whether agents retrieve the right Vektor memory context, use it faithfully, avoid stale memory, and expose missing information instead of guessing.
Designing Reliable Multi-Agent Workflows
Failure modes compound across orchestrated agents. The evaluation patterns we use to keep multi-agent systems honest — handoff schemas, loop detection, and memory coherence.
Why We Rebuilt Memory From Scratch
Memory v1 was a thin wrapper over a vector store. Memory v2 is a real system — three first-class stores, selective compression, and optimistic locking across agents.