Agent Architecture Articles
20 articles · Page 1 of 2
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.
The 17x error trap in multi-agent systems
Multi-agent systems amplify errors 17x, not reduce them. We compare CrewAI, LangGraph, and Autogen failure modes with concrete fixes and a decision tree.
AI Agent Frameworks Compared: Which Ones Ship?
An honest comparison of 9 AI agent frameworks (LangGraph, CrewAI, Vercel AI SDK, Mastra, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, Pydantic AI, AutoGen) based on what developers actually ship to production in 2026.
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.
The Multi-Agent Pattern That Actually Works in Production
Gartner reports a 1,445% surge in multi-agent system inquiries. Here are the orchestration patterns that actually work when real customers call -- and why most teams pick the wrong one.
Claude 4.6 broke our production agent in two hours — here's what's worth the migration
A practical developer guide to Claude 4.6 — adaptive thinking, 1M context, compaction API, tool search, and structured outputs. Real code examples in TypeScript and Python for building production AI agents.
Conversational AI vs. Agentic AI: What's the Difference, and Why It Matters for CX Teams
Conversational AI follows scripts. Agentic AI pursues goals. Here's the exact difference, with a side-by-side comparison and a practical guide to choosing the right approach for customer experience.
The Death of the Decision Tree: Why Rule-Based Bots Can't Survive Real Conversations
Scripted voicebots break the moment customers go off-script, which is most of the time. Here's exactly how decision trees fail, what agentic AI changes at the architecture level, and how to make the transition without a catastrophic cutover.
Agentic AI in Production: From Prototype to Reliable Service
Ship agentic AI that doesn't break at 2 AM. Covers orchestration patterns (ReAct, planning loops), error handling, circuit breakers, graceful degradation, observability, and scaling — with TypeScript implementations you can steal.
The Signal Briefing
One email a week. How leading CS, revenue, and AI teams are turning conversations into decisions. Benchmarks, playbooks, and what's working in production.
