6 min readgetting-started
Beads Task Tracking for Agent Chains
How to use Beads with Mentiko to track tasks, capture context, and maintain a clear record of agent chain work across your team.
beadstask-trackingintegrationworkflow
6 min readstrategy
Choosing the Right Model for Your Agents
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.
model-selectionllmcost-optimizationagents
6 min readgetting-started
Editing Agent Chains in the Browser
A walkthrough of Mentiko's visual chain builder: creating agents, wiring events, configuring workspaces, and exporting chain definitions -- all from the browser.
visual-builderchain-editoruigetting-started
5 min readproduction
Environment Variables and Secrets in Mentiko
How to manage API keys, configuration values, and sensitive credentials across agent chains without leaking them into logs or event files.
environment-variablessecretssecurityconfiguration
6 min readtechnical
GitHub Actions with Mentiko: CI/CD for Agent Chains
How to validate, test, and deploy agent chain definitions using GitHub Actions. Lint chain JSON, run dry runs, and auto-deploy on merge.
github-actionsci-cddeploymentautomation
7 min readproduction
LLM Trust and Supply Chain Risks: What You're Actually Running
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.
securitytrustsupply-chainmodel-evaluationrisk
6 min readproduction
Mentiko Best Practices: Building Reliable Agent Chains
Practical patterns for designing, testing, and operating agent chains that hold up in production. Chain structure, prompt design, error handling, and operational hygiene.
best-practicesreliabilityproductionpatterns
6 min readstrategy
The Open-Source LLM Landscape in 2026: What's Actually Worth Using
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.
open-sourcellmllamaqwenmistralmodel-selection
6 min readtechnical
Running Your Own Models on RunPod, Lambda Labs, and Beyond
A practical guide to self-hosting LLMs on GPU cloud providers and connecting them to your agent orchestration workflows.
self-hostedrunpodllminferencegpu
8 min readproduction
Capacity Planning for Agent Chains: How Much Compute Do You Need?
How to size infrastructure for AI agent workloads. CPU, memory, concurrent chains, and when to scale up vs out.
capacity-planninginfrastructurescalingperformance
7 min readtechnical
Concurrency Patterns for Agent Chains: Fan-Out, Fan-In, and Race
How to run agents in parallel, merge their outputs, and handle race conditions in multi-agent pipelines.
concurrencyparallel-executionfan-out-fan-inpatterns
7 min readproduction
Cost Allocation for Agent Chains: Charging Back by Team
How to track and allocate AI agent costs across teams and departments. Per-chain cost tracking, budget alerts, and chargeback models.
cost-managementchargebackbudgetingteams