Articles
Guides and analysis on agentage.wiki
Twelve in-depth articles by Zoltán Varga. Each one answers a single question, starting with the short answer.
Human Approval for Irreversible Agent Actions
A practical guide to classifying irreversible actions, designing effective previews, and preventing approval fatigue in autonomous systems.
Defending AI Agents Against Prompt Injection
Separate instructions from data, gate sensitive actions, and test your agents with hidden commands to prevent unauthorized data access.
Agent Loop Limits: Iteration, Cost and Termination Checklist
Treat guardrails as the boundary between experimental demos and reliable production systems.
Connecting Agents to Tools: MCP, Direct APIs, or CLI?
Governance requirements determine your tool interface; token efficiency only breaks ties between equally secure options.
Evaluating AI Agents: Building a Test Set That Means Something
Observation is not validation; build a test set that proves your agent works across real tasks, edge cases, and repeated runs.
Logging and Auditing AI Agents: What to Record for Reconstruction
A practical checklist for capturing the immutable data required to reconstruct every agent action, decision, and resource usage.
Single Agent or Multi-Agent? Decide by Task Structure
Parallelism is a property of the task, not a universal upgrade; decomposable work gains from fan-out while sequential work degrades under multi-agent overhead.
Writing Subagent Briefs and the Artifact Return Pattern
A practical guide to structuring subagent delegation and returning structured artifacts instead of raw text dumps.
Independent Agent Testing and Responsible Disclosure: FAQ
A practical guide for testers on isolating environments, defining attack scopes, and coordinating with developers before publishing vulnerability details.
When Verification Steps in Agent Pipelines Pay Off
Tokens spent on verification act as low-cost insurance, but only measured gains justify the expense in production systems.
The AI Agent Data Sheet: Fields and What They Mean
A practical guide to the specific data points required to document, compare, and verify autonomous AI agents in production.