Articles tagged “agent-architecture”
18 articles
MCP Webhooks: Build Event-Driven Agents That React in Real Time
MCP's request-response model breaks when AI agents need to react to external events. Build event-driven agents today with stateless HTTP and webhooks.
Your CX Agent Crashes Mid-Task. Here's the Fix.
When your CX agent crashes mid-refund or mid-booking, the customer is stuck. Durable execution guarantees long-running agent tasks survive failures. Here's how to build it.
Your Agent Is Already a State Machine. Make It Explicit.
Every production AI agent is secretly a state machine. Making it explicit gives you checkpointing, testable paths, and observable state transitions -- without rewriting your agent logic.
Why CX Agents Fail Between Conversations
Your AI agent handles the call perfectly and still fails your customer. The problem isn't the conversation -- it's everything that happens after it. Here's how async task queues fix the gap.
Past 50 tools, function-calling accuracy falls off a cliff
Past 50 tools, function-calling accuracy falls off a cliff. Measure the curve on your own agent and recover accuracy with per-turn toolset scoping.
1M-Token Context or RAG? How to Pick for Your CX Agent
Gemini's 1M-token window is real but not free. A practical decision framework for choosing between long-context and RAG for customer experience agents, with cost numbers, code, and the hybrid pattern most production teams land on.
Your Agent Should Use Three Models, Not One
Production CX agents route tasks by difficulty, not brand loyalty. The planner/router/summarizer pattern, a concrete rubric, support-deflection cost math, and the failure modes nobody warns you about.
When to Use a Supervisor, When to Let Agents Swarm
Supervisor burns 20-40% more tokens per run. Swarm hits a quality cliff past 8-10 handoffs. Start supervisor, graduate to swarm when latency bites.
The Modern Data Stack Wasn't Built for Agents
Snowflake, dbt, and Fivetran were built for humans asking batch questions. Agents need streaming signals, per-entity memory in under 100ms, and write-back.
Stop Storing Transcripts. Start Modeling Signals.
A JSON blob of transcripts works at 1k calls and collapses at 50k. Design a Signal schema with entity/event split, confidence, provenance, and versioning.
MCP Is Now Open Infrastructure: Build for What's Next
MCP was donated to the Linux Foundation and the AAIF just held its first summit. What does the protocol becoming open infrastructure mean for what you build on top of it?
Your MCP server is a monolith. Here's how to fix it
MCP servers dump every tool into the context window, burning tokens before your agent reasons. Four patterns to fix it: decompose, filter, gateway, facade.
50 Tools, Zero Memory. The Biggest Gap in AI Agents Today
AI agents can call 50 APIs but can't remember what you said yesterday. The tool layer is years ahead of the memory layer, and customers are paying the price.
The Buffering Bug That Quietly Breaks Voice Agent Latency
SSE streams fine locally, then tokens batch into 500ms bursts in production. Here's why, how to fix it, and why pipeline parallelism matters more than model speed.
Zero-Shot o sin oportunidad? Como los agentes de IA manejan llamadas que nunca han visto
Cuando un cliente llama con una solicitud que tu agente de IA nunca ha encontrado, que pasa realmente? Desglosamos la mecanica del manejo zero-shot y como probarlo antes de que falle en produccion.
MCP es ahora el estandar de la industria para integraciones de agentes de IA. Esto es lo que significa
MCP estandariza como los agentes de IA se conectan a herramientas y datos, reemplazando integraciones fragiles y propietarias con un protocolo universal. Esto es lo que significa para tus agentes.
IA Conversacional vs. IA Agentiva: Cual es la diferencia y por que importa para equipos de CX
La IA conversacional sigue scripts. La IA agentiva persigue objetivos. Aqui esta la diferencia exacta, con una comparacion lado a lado y una guia practica para elegir el enfoque correcto para experiencia del cliente.
Your agent has 30 tools and no idea when to use them
MCP tools give agents external capabilities. Skills give agents behavioral expertise. Learn the architecture of both, build them in TypeScript, and understand when to use each — and when you need both.
The Signal Briefing
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.
