MCP vs A2A vs ACP: The Three Protocols Every AI Agent Stack Needs
MCP connects agents to tools. A2A makes agents talk to each other. ACP connects them to editors. Three protocols, three layers — what each does, and when.
Zentask Blog5 min read
Multi-Agent Systems
444 stories
The Complete Connectome Explained: What a Finished Brain Map Tells Us About AI Agents (2026)
Scientists have now mapped an entire animal nervous system, neuron by neuron. What the map shows, what it cannot show, and why AI builders should care about it.
Taskade Blog14 min read
Claude Opus 5.5 Tested: What’s New and How Good is it?
What happens when an AI model gets better at reasoning, faster at responding, and cheaper to run at the same time? That is the promise behind Claude Opus 5.5, Anthropic’s latest flagship model and the first release in the Claude 5.5 family. Opus 5.5 brings several notable changes.
Analytics Vidhya9 min read
Jev Explained: The AI Model That Never Generates a Word of Text
If you follow trends in the AI world, chances are you have already come across Jev, a new AI model by TypeSafe AI. It is trending on X, and once you understand the reason behind it, you will want to try it out for yourself.
Analytics Vidhya9 min read
MCP vs A2A: Key Differences, Architecture, and When to Use Each
Compare MCP vs A2A across architecture, discovery, task state, security, and use cases, then learn when to use MCP, A2A, both, or neither.
Rerun Blog11 min read
Jev: System One models for Prod, not God — with Diogo Almeida, CEO, TypeSafe AI
We have an unusual relationship with today’s guest: for years since coauthoring the InstructGPT paper, Diogo Almeida had been saying that API-available frontier models have been going down the wrong path, everything from the alignment to refusals to reliability perspectives, that we have dropped…
Latent Space150 min readMuse connectors 🤖, Meta SAM 3.1 🖼️, Gemini hacks companies 🔓
Get advice from 15+ leaders on how to build and monitor reliable intelligent agents for better business outcomes in this book from Amazon Web Services (AWS) (Sponsor)
TLDR AI6 min read
OpenAI goes from hacker to hacked
OpenAI gets hacked while its own AI models were caught hacking. Plus: Build your own AI image skill pack in this week's briefing.
The Rundown AI8 min read
Canary evaluation for production AI agents: a practical guide
Silent quality drift in production AI agents is detectable within 24 to 48 hours using canary evaluation. Infrastructure metrics alone cannot catch it.
Prefactor Blog6 min read
OpenAI goes from hacker to hacked
OpenAI gets hacked while its own AI models were caught hacking. Plus: Build your own AI image skill pack in this week's briefing.
The Rundown AI Blog8 min read
The autonomous codebase
Five years into the AI-assisted coding experiment, I rarely find myself saying "wow" anymore. We, as an industry, have honed the prompt-to-code pipeline to just about the finest point imaginable.
Sourcegraph Cody Blog5 min read
Agentic AI vs Generative AI: From Prompt-Response to Autonomous Execution Architectures
The debate between agentic AI vs generative AI centers on the shift from content creation to autonomous execution. Generative AI creates text, images, and software code based on specific prompts. In contrast, agentic AI pursues goals independently by...
ClickRank Blog21 min read
Why 88% of agent pilots fail to reach production
88% of agent pilots never reach production. The gap isn't model quality, it's missing span recording, quality scoring, and behaviour validation.
Prefactor Blog6 min read
Multi-agent observability: spans, scores, and schema validation
Cross-boundary spans, per-agent quality scores, and schema validation isolate failures in multi-agent systems before they compound across the chain.
Prefactor Blog6 min read
Why 85% step accuracy collapses to 20% end-to-end success
At 85% per-step accuracy, a ten-step agent completes the whole task about one time in five. Final-output scoring cannot see this; trajectory evaluation can.
Prefactor Blog8 min read
Why multi-step agents fail in production but pass benchmarks
Step repetitions cause 17% of multi-agent failures and reasoning-to-action mismatches cause 14%, both undetected by final-output evaluation.
Prefactor Blog6 min read
How to detect agent misbehavior in production at scale
Behavior deviation detection, reasoning artifact capture, and adversarial prompt detection run continuously so human reviewers act on signals, not haystacks.
Prefactor Blog6 min read
How to detect unauthorized AI agent actions in production
Span-based instrumentation and activity schemas surface prompt injection, unauthorized access, and multi-turn drift before incidents compound.
Prefactor Blog8 min read
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