Agent Architecture Articles
53 articles · Page 3 of 5
Pre-Execute Tool Calls to Cut Agent Latency 48%
Sequential tool calls quietly kill your agent's response time. PASTE shows you can pre-execute likely tool calls during LLM thinking time and cut latency 48% without touching your model.
How AG-UI Connects AI Agents to Any Frontend
AG-UI is the open event protocol that connects AI agent backends to any frontend. Here's how it works, why the protocol stack now has three layers, and how to wire it into a real CX agent.
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.
Multi-Agent Systems Don't Fail at Reasoning. They Fail at Handoff.
Multi-agent systems don't fail at reasoning. They fail at handoff. Command objects, memory transfer, and the 8-10 handoff cliff, plus the telemetry that catches drift.
Everyone Benchmarks Opus. Your Chatbot Runs on Haiku.
Haiku 4.5, GPT-5 Mini, Gemini Flash at the $1/MTok tier that powers CX. Tool-call accuracy, first-token latency, structured-output reliability, blended cost math.
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.
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.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
