What happens when agents leave the demo?
Study real implementations through the lens that matters to builders: architecture, permissions, reliability, economics, failure modes, and measurable outcomes.
AI
AI Coding Agent Deployment Permissions: How to Control Production Releases
Allowing an agent to deploy code turns a coding assistant into a production operator.
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AI Coding Agent Database Access: How to Protect Production Data
A coding agent that can inspect or modify a database can create a much larger blast radius than source-code access alone.
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AI Coding Agents Need a Production Control Loop, Not Just a Prompt
AI coding agents can compress implementation time, but production quality still needs an explicit engineering loop.
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AI Agent Production Dashboards: What to Monitor After Launch
A production dashboard should answer whether agents are working, becoming expensive, getting slower, or creating risk.
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AI Agent Failure Classification: How to Debug Production Runs
Not every failed agent run has the same cause.
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AI Agent Evaluation Metrics: How to Measure Production Quality
Successful HTTP requests do not prove that an agent completed the right task.
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AI Agent Alerting: How to Build Useful Production Alerts
Alerting every time an agent fails creates noise and teaches teams to ignore monitoring.
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How to Evaluate an AI Agent Before Putting It Into Production
Stop testing agents with a few happy paths. Use a repeatable evaluation set that covers task success, tool use, failures, safety, latency, and cost.
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AI Agent Security Checklist Before Production
Use a practical pre-launch checklist covering identity, permissions, tools, secrets, sandboxing, prompt injection, approvals, logging, and rollback.
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How AI Agent Sessions and State Work in Production
A production agent needs separate ownership for request context, run state, sessions, durable business data, approvals, and audit events.
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AI Agent API Gateway Architecture for Production Workflows
A dedicated gateway can become the enforcement point between an agent and external services.
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If an AI Agent Can Touch Production, Your SaaS Needs a Permission Map
You do not need a security team to keep a one-person product honest. You need a short list of controls you can still explain at 2 a.m.
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AI Agent Permission Testing: How to Test Authorization Before Production
Permission bugs can remain invisible until an agent reaches the wrong resource. Build adversarial authorization tests before deployment.
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AI Agent Reliability: Retries, Timeouts, Fallbacks and Human Review
Reliable agents use bounded retries, explicit timeouts, fallbacks, idempotent writes, and human review for uncertain or high-impact operations.
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AI Agent Costs: How to Calculate the Real Cost Per Task
Agent cost is more than token price. Count model calls, tool calls, retries, sandbox time, and human correction against completed outcomes.
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AI Agent Error Handling: What Happens When the Model Fails?
Treat model failures as normal software states. Classify them, retry only when useful, and give the workflow a terminal failure state.
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AI Agent Observability: What You Should Log
Agent logs should reconstruct what happened without becoming a dump of sensitive customer data.
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AI Agent Evals: How to Measure Quality Before Launch
Build an eval suite that answers one question: does the agent complete the customer's task correctly and safely under realistic conditions?
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