Operations Articles
30 articles · Page 1 of 3
How to manage AI agent configs across environments
Your AI agent's system prompt, model settings, and tool definitions are config — and they deserve the same version control, review, and promotion workflow as your code. Here's how to build it.
How LLM Routing Cuts Agent Costs 40-85% in Production
Most CX agents send every request to their priciest model. Routing simple queries to a cheap model cuts costs 40-85%, at 90-95% of single-model quality.
The Six Agent Observability Platforms That Matter in 2026
Most observability platforms were built for LLM apps, not agentic CX systems. Here's how the six that matter in 2026 hold up against the criteria that count.
Most of your agent traffic doesn't need your best model
Routing 100% of agent traffic to your most capable model is costing you 6-10x what it should. Here's how to build a cascade router that handles simple tasks cheaply and escalates to stronger models only when the task demands it.
When your LLM provider goes down, your agent shouldn't
LLM providers go down. Anthropic's 90-day uptime is 98.95% -- that's 44 hours of potential outage per year. Here's how to build provider failover into production AI agents so a cloud incident doesn't become a customer experience incident.
Tool result caching: the latency and cost wins hiding in your stack
Your agent re-fetches the same data on every call. Tool result caching cuts latency by up to 70% and inference costs by 40-60% with changes that take days, not weeks. Here's how to classify, implement, and measure it.
AI agents blew their 2026 budget by April. Here's the fix.
One company burned through its entire 2026 AI budget by April. Here's why agent costs run away and how to stop it: per-session token caps, prompt caching for CX, model routing, and circuit breakers.
A quality control plane for your production agent fleet
Quality drift in production AI agents is silent. Build a quality control plane that scores every conversation, enforces SLOs, gates deployments, and alerts on drift.
SRE for AI Agents: SLOs, Error Budgets, and Reliability
Traditional SRE doesn't catch AI agent failures. Here's a practical SRE playbook for agents: the five SLIs that matter, how to set SLOs that are actually useful, and how error budgets control agent autonomy before problems escalate.
The Agent Development Lifecycle: Ship, Observe, Improve
Shipping an AI agent is easy. Keeping it reliable after launch is where most teams struggle. The ADLC gives you a structured approach: Intent, Build, Evaluate, Deploy, Observe -- and then do it again.
Your agent re-reads its own manual on every call
Datadog's 2026 State of AI Engineering report found that 69% of input tokens go to system prompts, yet only 28% of LLM calls use prompt caching. Here's how to diagnose the problem and fix it without rewriting your agent.
Shadow Mode: Deploy AI Agent Updates Without Risk
Shadow mode runs your new agent version in parallel with production, comparing behavior before customers ever see it. Here's how to build the full deployment pipeline from shadow to canary to 100%.
El briefing de Signal
Un email por semana. Cómo los equipos líderes de CS, ingresos e IA están convirtiendo conversaciones en decisiones. Benchmarks, playbooks y lo que funciona en producción.
