Agent Architecture Articles
53 articles · Page 1 of 5
Your Agent Is Only as Good as Its Harness
Upgrading the model rarely fixes a failing agent. The harness, the code that manages tools, memory, and context, is where agent quality actually lives.
Agent Behavioral Contracts: Stop Drift Before It Hits Callers
Behavioral drift happens with no prompt change and no failing test. Agent behavioral contracts enforce rules at runtime and flag drift before callers do.
Why 40% of multi-agent systems fail in production
Nearly half of multi-agent CX pilots fail within six months of production — not because the AI is wrong, but because teams don't understand how multi-agent systems break. Here are the five failure modes and how to diagnose each.
How to rescue a stuck CX agent conversation
Conversations get stuck in four predictable patterns: clarification spirals, topic drift, assumption deadlocks, and initiative mismatches. Here's how to detect each one mid-flight and apply the repair move that actually works.
A2A and MCP: the agent protocol stack explained
MCP connects agents to tools. A2A connects agents to other agents. Together they form the full protocol stack for multi-agent CX systems. Here's how they work and why you need both.
Reasoning models for CX agents: when to think slow, when to move fast
Extended thinking models improve decision quality for complex CX scenarios, but add 5-30 seconds of latency and cost 3-5x more. Here's the planning-execution architecture that gets you the benefit without the tradeoff.
One agent per customer: the CX shift that's here
Most CX agents reconstruct context from a database on every session. The next model gives each customer their own persistent agent instance that carries state between conversations. Here's what that architecture looks like and why it matters.
On-device AI agents for CX: what edge deployment unlocks
On-device AI agents are no longer a research project. Here's what running your CX agent on local hardware actually unlocks in 2026, and what it doesn't.
How to debug the agent conversation you can't reproduce
AI agents fail in ways you can't reproduce by resending the same message. Deterministic replay captures what the agent saw and did so you can step through the failure exactly as it happened.
Extended thinking for CX agents: when slow beats fast
Reasoning models (Claude Opus 4.7, o3, Qwen 3) let your agent think through complex problems before acting. For some CX tasks that's a major improvement. For others, it's a 4-second pause before telling someone their order ships Thursday.
Stop Spending Tokens on Agent Routing
Microsoft's open-source Conductor CLI routes multi-agent CX workflows with YAML and Jinja2 -- zero tokens spent on orchestration. Here's when to use it and when to stick with LLM-driven routing.
Your CX agent needs an autonomy level before it goes live
Not all CX agents should be equally autonomous. Here's a practical L0-to-L4 framework for deciding how much independence to give your agent for each action type, and what eval thresholds to hit before promoting.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
