Deep dives on AI agents, multi-agent architecture, MCP integrations, and building production-grade intelligent systems.
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Monolithic LLM prompts are the equivalent of writing all your business logic in a single function. Microagents — small, single-purpose AI components — compose into systems that are testable, replaceable, and dramatically cheaper to iterate on.
Recent Articles
Google’s Agent-to-Agent (A2A) protocol and Anthropic’s Model Context Protocol (MCP) are not competitors — they solve different layers of the same problem. MCP connects an agent to tools; A2A connects agents to agents. Together they form a complete inter-agent communication stack, and AgentDyne now supports both.
Sequential pipelines waste wall-clock time. When three independent agents can run in parallel, running them sequentially is the AI equivalent of a single-threaded server. We explain the DAG-based parallelism engine behind AgentDyne Pipelines, how we detect branch nodes, and why continue_on_failure changes the error calculus entirely.
Everyone can generate a working agent in five minutes with a frontier model. Fewer than 5% of those agents are still working six months later. The gap isn’t the model — it’s observability, schema validation, cost controls, and version pinning. This is the production checklist we wish existed when we launched AgentDyne.
The Model Context Protocol is quietly standardising how AI agents connect to external services. If every agent had to re-implement its own GitHub or Notion integration, the ecosystem would fragment. MCP prevents that — and AgentDyne has 40+ verified MCP servers ready to plug in.
Retrieval-Augmented Generation (RAG) lets your agents answer from facts, not imagination. We walk through the exact chunking strategy, embedding model choice, and pgvector cosine-similarity queries that power AgentDyne's native knowledge bases.
Just as DNS maps domain names to IP addresses, an Agent Registry maps task descriptions to capable agents. We explain how AgentDyne's registry uses composite quality scores, capability tags, and routing heuristics to automatically select the best agent for any job.
After running 10,000 pipeline executions across our beta users, here is what we learned: where timeouts blow up, how to design idempotent nodes, when to use continue_on_failure, and why output schemas matter more than system prompts.
Prompt injection attacks let malicious users override your system prompt, extract secrets, or impersonate the AI. We open-source our 18-pattern injection filter that blocked 4,200 attacks in the first month of production — and explain why regex beats ML for Layer 1 defence.
Most SaaS platforms take 30–50% as a platform fee. We take 20%. The reason is not altruism — it is growth strategy. When builders earn meaningful money from their agents, they invest more in making them excellent. We are betting on that flywheel.
Cold starts kill agent UX. We migrated from Vercel to Cloudflare Pages (via @cloudflare/next-on-pages) and cut cold start time from 800ms to under 50ms globally. Here is the trade-offs, the worker isolation gotchas, and the in-memory rate-limiter problem we hit.
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