Why AI customer support replies sound generic
AI support replies sound generic because teams treat brand voice as a prompt problem. Context engineering fixes it by selecting the right exemplars.
Further reading
Articles about AI Agent
35 articles from the Wire blog, sorted newest first. Return to the AI Agent definition for context.
TOON vs JSON: why smaller doesn't mean cheaper for LLMs
TOON looks more compact than JSON, but a 9,649-test study found it cost LLMs 38% more tokens. The reason: model training distribution beats format size.
GPT-5.5 didn't cut hallucinations 60%. Here's what it did.
OpenAI's GPT-5.5 system card reports 23% better claim-level accuracy, not the 60% hallucination reduction making press rounds. Here's what actually changed.
Agent drift: why long-running AI agents lose the plot
Agent drift is how AI agents silently deviate from goals over long-running tasks. Six mechanisms cause it, and most have nothing to do with the model.
Provenance is a context engineering primitive, not a trust score
Retrieval provenance for AI agents isn't an audit log or a trust verdict. It's structural metadata (source, position, time, edges) agents use to plan.
One job per tool: why adding wire_navigate cut agent calls 24%
We restructured Wire's MCP surface from 2 overloaded tools to 3 single-purpose ones. The counterintuitive result: adding a tool cut total calls 24%.
GPT-5.4-pro hallucinates more than GPT-5.4-nano
Vectara's 2026 benchmark shows OpenAI's flagship GPT-5.4-pro hallucinates at 8.3% while its nano variant stays at 3.1%. The reasoning-model tradeoff, explained.
How to build a private AI second brain
Native Notion and Obsidian MCP give every connected agent the same coarse scope. Build a private AI second brain with per-agent, revocable access across tools.
Context budgets: how to allocate tokens for AI agents
A practical guide to context budgets for AI agents. How to allocate tokens across system prompts, tools, retrieval, history, and a buffer in production.
Why your AI costs are a context problem
Token prices fell 280x but enterprise AI spend rose 320%. Poor context architecture drives 60-70% of total AI costs. Here is where the money actually goes.
How to connect AI to private data safely
77% of employees share sensitive data with AI tools. Five context engineering patterns give AI what it needs without exposing what it shouldn't see.
Context compression: why less context means better AI
Context compression reduces AI agent memory usage by 26-54% while preserving task performance. Here's how it works and why bigger context windows aren't the answer.
How prompt caching cuts AI agent costs by 90%
Prompt caching reduces AI agent API costs by up to 90% and latency by 31%. Here's how it works, where it breaks, and how to implement it right.
Why Customer Support AI Gives Wrong Answers
AI customer service fails at 4x the rate of other AI tasks. Support bots need five types of context most teams never provide. The model isn't the problem.
Why AI agents forget mid-task (and how to fix it)
65% of agent failures come from context drift, not token limits. Here's how context compression keeps long-running AI agents on track.
Why AI Agent Memory Keeps Failing
AI agent memory fails because it's a context engineering problem, not a storage problem. Research reveals three failure modes and what actually works.
Why AI Coding Assistants Can't See Your Codebase
84% of developers use AI coding tools, but only 29% trust the output. The problem has less to do with models and more to do with codebase context.
How to measure context quality for AI agents
Five dimensions of context quality that determine AI agent performance, with metrics, benchmarks, and practical measurement approaches for production systems.
Why Semantic Search Beats Keywords for AI
Hybrid search improves AI retrieval accuracy by up to 41% in technical domains. Here's how semantic search works, where keywords fail, and when you need both.
Why Product Management AI Gives Wrong Priorities
84% of product teams doubt their products will succeed despite AI adoption. The problem: PM tools see feature requests but not the context behind what to build.
Why Sales AI Fails Without Deal Context
87% of enterprises missed revenue targets despite AI investment. Sales AI needs five types of deal context most teams never provide. The model isn't the issue.
Why multi-agent AI systems fail at context
Up to 86.7% of multi-agent AI runs fail. Most failures trace back to how agents share context, not the agents themselves. Here's why and how to fix it.
7 context engineering techniques for production
Seven context engineering techniques used in production AI systems, with implementation patterns, research backing, and guidance on when each one works.
Structured Context vs Raw Text for AI
ETH Zurich found AI-generated context files hurt agent performance by 3%. Format choice alone swings LLM accuracy by 40%. Here's what the research says.
Why MCP failures are a context engineering problem
New research analyzed 3,282 MCP bug reports across GitHub. The patterns reveal a context delivery problem, not a protocol problem. Here's what it means.
AI agents have too much access
88% of organizations report AI agent security incidents. The root cause is a context engineering failure: agents get all-or-nothing access, not scoped context.
Why AI Hallucinations Are a Context Problem
GPT-5.2 hallucinates at 10.8%, o3-pro at 23.3%. The fix has less to do with better models and more to do with context engineering. Here's the research.
Context engineering: the end of prompt engineering
Prompt engineering is a dead end. Context engineering — designing what information AI models receive — is replacing it. Here's how to start applying it.
Your AI Doesn't Remember You. That's by Design.
94% of IT leaders fear vendor lock-in. Every AI tool traps your context in its own silo. Here's why your AI doesn't remember you, and what's changing.
Why Does ChatGPT Forget Everything?
AI doesn't forget because it's broken — it forgets because everything gets crammed into one place. Here's the technical explanation and how to fix it.
Five Ways to Give AI Access to Your Data
From copy-paste to context platforms, five approaches to giving AI access to your data. Covers security trade-offs, cost, and practical recommendations.
How to Get an MCP Server Without Writing Code
Over 17,000 MCP servers exist but most are generic dev tools. Here's how to create a custom one for your own data without writing a single line of code.
The AI Silo Problem: Why Your Tools Don't Talk to Each Other
76% of enterprises suffer from disconnected AI tools. Your tools don't share context, and it's costing you performance. Here's what unified context looks like.
RAG Is Not Enough: When Retrieval Fails Your AI
RAG is a context-building strategy, not magic. Research shows 70% of retrieved passages miss the mark. Here's why naive retrieval fails and what works.
Context Rot: Why AI Performance Degrades With More Information
Research shows LLMs drop from 95% to 60% accuracy as context grows stale. Here's how context rot degrades AI performance and why bigger windows won't help.
Put context into practice
Create your first context container and connect it to your AI tools in minutes.
Create Your First Container