Articles tagged “agent-architecture”
50 articles
Build Context Editing Into Your Agent and Cut Token Use 84%
Stale tool results pile up in long conversations, raising cost and hurting accuracy. Context editing and the memory tool fix both. Here's how to build them.
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.
How to Build an Agent That Writes Code Instead of Calling Tools
One JSON tool call per turn is slow, token-heavy, and brittle. A short script that orchestrates tools in a sandbox is faster and cheaper. Here's how it works.
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.
Your agent doesn't need more memory. It needs a graph.
Session memory forgets. RAG retrieves but doesn't reason. Context graphs give your CX agent a persistent, queryable model of every customer, issue, and relationship it's ever encountered -- at a fraction of the token cost.
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.
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.
MCP's 2026 spec changes what your tools can do
The MCP spec RC is out with Extensions, Tasks, MCP Apps, and OAuth hardening. Here's what changes for CX agent builders before the July 28 final.
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.
Agent warmup: preload customer context before the first word
Most agents ask for information they already have. Here's how to preload the right customer context before an interaction starts, so your agent sounds like it knows the customer from word one.
What the July 28 MCP spec means for your stateful agents
The July 28 MCP spec is the largest protocol revision since launch. Sessions are removed. Stateless request handling replaces the initialization handshake. Here's what breaks and how to fix it before the deadline.
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.
What your agent does when it knows nothing about a customer
The cold-start problem hits every CX agent on first contact: no history, no profile, no context. Here's a practical architecture for handling first-contact customers without making them feel like strangers.
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.
The delegation gap: what you can hand off to your agent
Anthropic's 2026 report found teams use AI in 60% of work but fully delegate only 0-20%. The same gap hits CX teams. Here's a framework for closing it.
Agents that learn while they sleep: async memory consolidation
Post-session async consolidation is the missing layer in most agent memory stacks. Here's how Anthropic's Dreaming primitive works, why Harvey saw a 6x jump, and how to implement the same pattern without Anthropic's API.
Parallel tool calls: the 5x speedup in your agent
Sequential tool calls compound latency for no reason. Here's how to fan out independent tool calls in parallel and cut agent response time by up to 5x in production.
Prompt caching: the cost cut most agent teams skip
Prompt caching cuts API costs 41-80% and TTFT by up to 31%. Learn where to put the cache breakpoint, when it backfires, and how to monitor hit rates in production.
Design agent tools that won't double-charge on retry
Agents retry when tools fail. Without idempotency, retries create duplicate records, double charges, and corrupted state. Here's the engineering pattern that makes every mutating tool safe to retry.
Five things your CX agent needs that its framework won't provide
LLM frameworks handle conversations. They don't handle memory, tool reliability, pre-production testing, or observability. Here's what every production CX agent actually needs.
When your agents call other agents, don't assume trust
Multi-agent systems create delegation chains where each agent trusts the one that called it. That assumption is how prompt injection and privilege escalation get in. Here's how to enforce scoped trust instead.
MCP tasks: how async tool calls fix your agent's timeout problem
The November 2025 MCP spec introduced Tasks: a call-now, fetch-later primitive that lets agents kick off slow operations without blocking. Here's how it works and how to build with it.
When voice becomes chat: carrying context across channels
When a customer switches from a call to chat, your AI agent loses everything it learned on the phone -- unless you package and transfer context deliberately. Here's how to build channel context handoffs that actually work.
How to Build Ambient AI Agents for Always-On CX
Most AI agents wait for prompts. Ambient agents watch event streams and act first. Here's how to build always-on CX intelligence that catches problems before customers notice them.
How to Build Agent Interrupt and Approval Checkpoints
How to pause an AI agent before high-stakes actions, persist full state through the approval window, and resume cleanly. Covers interrupt gates, approval queues, checkpointing, and EU AI Act compliance for production CX agents.
How to Build Idempotent Tool Calls for AI Agents
Naive retry logic charges customers twice, sends duplicate emails, and fires double webhooks. Here's how to build idempotent tool calls for AI agents with idempotency keys, deduplication, and safe retries.
How to Build the Context Package for AI-to-Human Handoffs
AI agents escalate every day, and most send the human in blind. Here's how to build the context package that makes handoffs invisible to customers.
How to Write an Agent Spec Before You Write the Prompt
Inconsistent agent behavior isn't a prompt problem. It's a missing-spec problem. Here's the seven-section document that fixes it before code.
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.
MCP Webhooks: Build Event-Driven Agents That React in Real Time
MCP's request-response model breaks when agents need to react to external events. Here's how to build event-driven agents today using stateless HTTP plus webhooks, and what the June 2026 spec will make native.
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 or Zero Chance? How AI Agents Handle Calls They've Never Seen Before
When a customer calls with a request your AI agent has never encountered, what actually happens? We break down the mechanics of zero-shot handling, and how to test for it before it fails in production.
MCP Is Now the Industry Standard for AI Agent Integrations. Here's What That Means
MCP standardizes how AI agents connect to tools and data, replacing fragile, proprietary integrations with a universal protocol. Here's what it means for your 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.
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.
Learn Agentic AI
Weekly. Patterns for shipping agents that work. MCP, scorecards, regression tests, prompts, model comparisons.
