Agent infrastructure blog
Seven Vektor posts for teams evaluating, tracing, and operating production AI agents. The series covers routing, full-span traces, semantic caching, tool governance, release gates, prompt releases, and memory evaluation.
This blog series links to seven Vektor posts about production agent infrastructure. Each post is part of /blog/agent-infrastructure, links to related Vektor product pages, and includes a short FAQ.
All posts
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.