No. 034 · Models
Why AI tools can finally talk to each other without custom wiring
Two protocols — MCP for tools, A2A for agents — are turning bespoke integrations into shared plumbing, now handed to a neutral foundation.
· 13 min read
Blog · one trend at a time
DM Explains
One new AI trend at a time, taken apart properly: how it works, where it breaks, and whether you should use it.
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No. 033 · Orchestration
The AI gateway: one front door for every model your company calls
As teams wire apps and agents to dozens of model providers, the sprawl of point-to-point connections becomes the real risk. An AI gateway turns that sprawl into one governed chokepoint for cost, security and routing.
· 13 min read
No. 032 · Governance
Security guardrails: why screening the prompt isn't enough anymore
Prompt filters catch bad text, but the 2026 data says the bigger danger is what agents are allowed to do once they're inside — not what they were told.
· 14 min read
No. 031 · Orchestration
Multi-modal orchestration: why one supervisor beats one giant model
Instead of training one model that sees, hears and reads, you can wire together specialists that already do each job well — and a controller that decides who answers what.
· 13 min read
No. 029 · Governance
Security-aware agent federation: AI agents cooperating without a spying boss
A cluster of new protocols lets AI agents from rival organisations work together and prove the system is healthy, without any single operator seeing what they actually did.
· 16 min read
No. 028 · Agents
Agent federation: how thousands of AI agents find each other without melting the network
As organisations spin up hundreds of specialised agents, wiring them together directly stops working. Federation replaces wiring with advertisement, matching and a shared noticeboard.
· 15 min read
No. 027 · Architecture
Protocol federation: how AI agents will talk to tools and each other
MCP lets an agent use a tool. A2A lets one agent ask another for help. Neither yet lets a downstream agent prove who sent it.
· 15 min read
No. 026 · Architecture
Context rot: why your AI agent can be confident, well-formed and still wrong
An agent can produce a correct-looking answer from context that is stale, partial or scoped to the wrong task — and neither the model nor the pipeline will report an error.
· 15 min read
No. 025 · Orchestration
Context-aware orchestration: why agents need a context manager, not just a bigger window
Dumping the whole conversation into the model every time is not reliability, it's waste. The 2026 systems that win budget what the model sees, step by step.
· 13 min read
No. 024 · RAG
Multi-modal RAG: making one search find the photo, the recording and the page
Most company knowledge isn't text. Here's how retrieval systems learn to search images, audio and video alongside documents, and where stitching them together still breaks.
· 15 min read
No. 023 · Orchestration
The multimodal orchestrator: why no one is training one giant model anymore
Instead of building one model that understands text, images, audio and video at once, teams are wiring together specialists through a controller that decides who does what. It's cheaper, and sometimes better.
· 15 min read
No. 022 · Governance
Secure multi-agent federation: who checks an AI agent's ID at the border
When an agent from one company calls an agent or tool in another, nothing standard proves who it is, what it may do, or who it answers to. A cluster of 2026 IETF drafts is trying to fix that.
· 15 min read
No. 021 · Governance
Secure federation: how AI agents from different companies trust each other
Proving an agent's identity is nearly solved. The real fight is over how little authority it hands to the next agent in the chain.
· 13 min read
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