Jev AI Model: The Fastest Adopted Model in AI Gateway History
Jev reached nearly 13% of paid AI Gateway teams unusually fast. Here is what that adoption curve means for gateway and model selection.
Category
AI
These AI guides cover language models, agents and the tools I use to ship real features. The aim is simple: less hype and more working detail.
For example, start with the MCP explainer or AI agent harnesses. After that, the AI tools matrix compares what is worth adopting. Each post also points to the deeper series under AI Agents, Claude and n8n.
40 articles
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Tool Sandboxing and Permission Models: Least Privilege for Agents
Tool sandboxing and permissions for AI agents: least privilege, role tiers, capability isolation, and sandboxes that actually limit…
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Prompt Injection in Agents: Indirect Attacks Through Tools and Data
How prompt injection in agents works through tools and data, and the defense-in-depth stack: least privilege, output validation,…
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Agent State Persistence: Checkpointing and Resumable Workflows
Agent state persistence explained: checkpoints, resumable runs, replay safety, versioned state schemas, and how to resume without repeating…
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Cost Control: Token Budgets, Caching, and Model Routing
AI agent cost control: token budgets, prompt caching, model routing, and context discipline, measured as cost per successful…
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Reliability for Agents: Retries, Timeouts, Fallbacks, Circuit Breakers
Reliability for agents: the failure taxonomy plus retries, timeouts, fallbacks, circuit breakers, idempotency, and bounded execution that keep…
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Deploying AI Agents: Architecture and Patterns
Deploying AI agents in production: execution boundary, gateway, orchestrator, tool gateway, state stores, queues, workers, and sync vs…
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Tracing Agent Runs: LangSmith, Langfuse, and OpenTelemetry
Tracing agent runs: spans for model calls, tools, retrievals, and state changes in LangSmith, Langfuse, or OpenTelemetry, without…
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How to Evaluate AI Agents: Task Success, Trajectory, and Cost
Evaluate AI agents beyond answer quality: task success, tool and argument correctness, trajectory quality, cost per successful task,…
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Context Window Management: Summarization, Compression, Pruning
Context window management for AI agents: budgeting, summarization, compression, and pruning, so agent runs keep the right state…
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RAG for Agents: Retrieval That Adapts to the Task
RAG for agents vs chatbot retrieval: task-aware queries, grounding, access control, freshness, and retrieval as a tool the…
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Multi-Agent vs Single-Agent: When Multi-Agent Wins (and When It Doesn’t)
A cost-benefit test for multi-agent vs single-agent: when specialization and isolation pay off, and when one agent loop…
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Multi-Agent Architectures: Supervisor, Swarm, and Hierarchical
Supervisor, swarm, and hierarchical multi-agent architecture compared on coordination, shared state, latency, cost, and how failures spread between…
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MCP for AI Agents: How the Model Context Protocol Standardizes Tools
MCP for AI agents: how the Model Context Protocol standardizes tools, resources, and prompts, and what it leaves…
