· ai-agent
Anthropic's Managed Agents memory: what it changes
Anthropic launched Memory for Managed Agents on April 23, 2026 in public beta. What the design means for agent scope, freshness, and context engineering.
Blog
The Wire Blog
Practical context engineering for AI agents: how to structure, deliver, and manage the context that determines whether AI systems actually work.
Research-backed articles on context architecture, retrieval, agent security, and making AI tools more effective. Written by Jitpal Kocher.
· MCP (Model Context Protocol)
What MCP's 2026 roadmap means for context delivery
The MCP 2026 roadmap reframes Model Context Protocol as enterprise context infrastructure: stateless transport, MCP Apps SEP-1865, audit logs, SSO auth.
· Multi-Agent System
Why every agent handoff corrupts your context
Every multi-agent handoff is a lossy compression event. Learn which five types of context degrade at agent handoff boundaries and how to preserve them.
· Tool Poisoning
Tool poisoning: how MCP tool descriptions hijack agents
Tool poisoning hides instructions inside MCP tool descriptions the agent reads as trusted context. The MCPTox benchmark recorded a 72.8% attack success rate.
· AI Agent
Tool-based agent memory: why 2026 benchmarks favor it
Tool-based agent memory exposes store, retrieve, and navigate as callable MCP tools. 2026 benchmarks from Mem0, Memanto, and Wire show why the pattern wins.
· Context Engineering
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.
· Structured 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.
· AI Hallucination
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
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.
· Context Engineering
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.