AI Workflows & Agentic Architecture Directory

Pinecone vs Weaviate vs Milvus vs pgvector benchmarked for 2026 agent workloads: hybrid search, HNSW, sub-100ms ANN latency, cost, and the economics that make RAG about 1/10th the cost of fine-tuning.

Agent memory is the strategic layer of 2026 AI systems: long-term stores, working memory, and context engineering. Here is the agentic memory stack, the universal memory pattern (mem0, ~54K stars), and its cost economics.

Computer-using agents (CUA) in 2026 use computer vision to operate browsers and desktop apps in an observe-plan-act loop — with MCP wiring into VS Code and JetBrains. Here is the architecture, safety railings, and cost model.

Google ADK runs on GCP, speaks A2A natively, and sees multimodal through Gemini. A deep-dive for engineers building enterprise multi-agent fleets with Gemini in 2026.

Evaluation in production is a capital-F Feedback loop: capture traces, promote hard ones into datasets, run regression suites, and gate each deploy. Every robust 2026 AI team works this way.

OpenAI Agents SDK (provider-agnostic via LiteLLM, ~10.3M downloads) vs PydanticAI (type-safe durable). For Python teams the decision grounds in runtime flexibility versus hygienic type-safety.

Production-grade observability for multi-agent systems needs three layers: OpenTelemetry for standardized signal emission, OpenInference for LLM-aware semantics, and Langfuse for trace analytics. This workflow wires all three into a CrewAI or LangGraph fleet without forking your agents.

Agents speak two protocols: MCP for tools and A2A for agents. This workflow builds a FastAPI interoperability gateway that bridges Google ADK and LangGraph, exposing A2A inbound while multiplexing shared MCP tool servers to both frameworks.

Make LangGraph agents durable and auditable with Postgres checkpointing, Pydantic event-sourced state, crash recovery, and time-travel replay. This workflow does not just survive a crash, it rewinds to any past state and forks a new future.

Run Langfuse as an AgentOps observability plane reachable from Claude Desktop: OpenTelemetry GenAI semantic conventions, gen_ai.* trace ingestion, dual-device session replay, and per-trace cost tracking over a native MCP server at /api/public/mcp.

Bridge Anthropic's MCP tool surface to Google/A2A's agent-to-agent task lifecycle: discovery via Agent Cards, task-state negotiation, and a single gateway that turns any MCP tool into a delegable A2A task for the enterprise.

A grounded 2026 cost-benefit analysis of Langfuse, AgentOps, and LangSmith for tracing, debugging, and growing agentic AI in production — including token economics, pricing, and where each genuinely wins.