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.
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501 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…
