AI systems need architecture — design them with current context
RAG pipelines, model routing, agent orchestration, vector databases — AI architecture patterns are evolving weekly. Get a brief that keeps your designs current.
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AI architecture patterns change fast
Six months ago everyone built RAG. Now it's agentic workflows. The reference architectures keep shifting and your designs need to keep up.
Model selection affects entire system design
Choosing between cloud APIs, open source models, and fine-tuned variants has cascading effects on architecture, cost, and latency.
Integration complexity is growing
Connecting LLMs to enterprise data, existing APIs, and business logic requires new patterns that aren't in any textbook yet.
AI news through the Solutions Architects lens
Architecture-focused coverage
We cover AI from a systems design perspective — integration patterns, infrastructure choices, and scalability considerations.
Reference design updates
Evolving reference architectures for RAG, agents, model serving, and AI-enhanced applications.
Vendor-neutral analysis
Honest comparisons of cloud AI services, vector databases, and orchestration frameworks.
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News Relevant to You
AWS Bedrock Adds Multi-Agent Orchestration Framework (March 2024)
Amazon Web Services expanded Bedrock with native agent orchestration capabilities, enabling architects to build complex AI workflows without external frameworks. The update includes built-in state management and inter-agent communication patterns.
Why this matters to you: Your agent orchestration patterns just got a first-party option on AWS—understanding this new architecture could eliminate a vendor dependency in your AI infrastructure stack.
Pinecone and MongoDB Announce Unified Vector-Relational Index (February 2024)
The two platforms launched a joint index format allowing seamless queries across vector and relational data. This reduces architectural complexity for retrieval-augmented generation systems that need both embedding and structured filtering.
Why this matters to you: If you're designing vector database selections for RAG systems, this reference design simplifies the choice between separate tools and reduces your overall model serving complexity.
What To Test This Week
Compare Latency Profiles: vLLM vs. TensorRT-LLM for Your Inference Workload
Spin up identical model endpoints using both vLLM and NVIDIA TensorRT-LLM on the same hardware, then measure p50/p99 latencies under concurrent request loads. Document token throughput and memory utilization across batch sizes.
Why this matters to you: Model serving architecture choices directly impact your cloud AI infrastructure costs and user experience—this test will give you concrete data to defend your architecture decisions.
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Topics we watch for you include
- 🔍AI architecture pattern evolution and best practices
- 🔍Cloud AI service updates (AWS, Azure, GCP)
- 🔍Vector database and retrieval infrastructure comparisons
- 🔍Agent orchestration and workflow design patterns
- 🔍Weekly reference designs to evaluate for your systems
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