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108 articles on agent orchestration, architecture, and automation
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A decision framework for matching LLMs to agent tasks. When to use Claude, GPT, or open-source models based on cost, quality, speed, and task requirements.
A walkthrough of Mentiko's visual chain builder: creating agents, wiring events, configuring workspaces, and exporting chain definitions -- all from the browser.
How to manage API keys, configuration values, and sensitive credentials across agent chains without leaking them into logs or event files.
How to validate, test, and deploy agent chain definitions using GitHub Actions. Lint chain JSON, run dry runs, and auto-deploy on merge.
Model provenance, training data risks, licensing traps, and the supply chain problem in AI. How to evaluate whether you can trust the models in your pipeline.
Practical patterns for designing, testing, and operating agent chains that hold up in production. Chain structure, prompt design, error handling, and operational hygiene.
A practical assessment of the current open-source model ecosystem -- Llama 4, Qwen 3.5, Mistral, DeepSeek, and Command R+ -- with honest strengths and weaknesses.
A practical guide to self-hosting LLMs on GPU cloud providers and connecting them to your agent orchestration workflows.
How to size infrastructure for AI agent workloads. CPU, memory, concurrent chains, and when to scale up vs out.
How to run agents in parallel, merge their outputs, and handle race conditions in multi-agent pipelines.
How to track and allocate AI agent costs across teams and departments. Per-chain cost tracking, budget alerts, and chargeback models.
Break down every cost component of running multi-agent pipelines: LLM tokens, compute, orchestration platforms, and the hidden costs nobody talks about.