Build AI agents that plan, call tools, and complete multi-step tasks autonomously. Explore agent frameworks, orchestration patterns, MCP servers, and multi-agent workflows with real implementation examples. Learn the patterns behind reliable, production-grade agents instead of fragile demos.
A roundup of the MCP servers built for multi-agent orchestration and autonomous workflows — swarm orchestrators, workflow engines, and durable execution substrates — plus what the context layer contributes and how these servers differ from plain tool servers.
MemGPT treats the LLM context window as RAM and external storage as disk, paging data between main context, recall storage, and archival storage through agent-directed tool calls under memory pressure — letting long-running agents exceed fixed window limits.
A technical walkthrough of the open-source autonomous BI agent's sandboxed scratchpad, its session/semantic/long-term memory layers, the credential vault model, and the open-source versus managed deployment split — plus how the analyst role shifts from query writer to steward and trainer.
A revenue-ops oriented guide to the Slack, Notion, and Salesforce MCP servers, including the documented Sales Call Follow-up template that automates post-call admin end-to-end.
A step-by-step dissection of one iteration of the harness loop, from prompt assembly through tool execution and observability, grounded in Claude Code and OpenDev internals.
A side-by-side comparison of MindsDB's federated SQL approach against traditional ETL pipelines for AI workloads, covering freshness, latency, operational cost, and the breakpoints where each architecture fails.
An architecture-focused comparison of supervisor, peer-to-peer, adversarial, and graph-based multi-agent coordination patterns, with trade-offs, failure modes, and selection criteria.
A deep dive into the Memory MCP reference server's knowledge-graph persistence model, comparing it to vector stores and showing how to wire it into a client for long-term agent context.
A practitioner's guide to using a knowledge graph as durable agent memory, with Graphiti's EntityNode and episode pipeline as the concrete read/write loop.
A practical buyer's guide that compares Caveman variants against external alternatives, helping readers pick a compressed-output skill based on workflow, model, and stack rather than headline token-savings numbers.