How Engineers Are Building Reliable AI Agents for Hospital Appointment Intake

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A technical blueprint published on DEV Community outlines the architecture of an autonomous AI concierge system designed for enterprise-level hospital networks. The system addresses common failures in standard chatbot pipelines, such as forgotten patient details, hallucinated scheduling data, and duplicate records in electronic health systems. The proposed design separates conversational language processing from deterministic state machines to ensure reliable, step-by-step patient intake. It incorporates sub-100ms external scheduling tool calls, idempotent database writes to prevent duplicate entries, and retrieval-augmented generation for grounded clinical FAQ responses. The architecture aims to make AI-driven healthcare intake safer and more predictable in production environments.

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Open-Source Dashboard Tracks Token Usage, Quotas, and Agent Activity in Antigravity

A developer has released Antigravity Usage Intelligence, an open-source analytics dashboard built to provide detailed visibility into how Antigravity AI is being used during development sessions. The tool breaks down token consumption into distinct categories — fresh input, cached prompt, output, and reasoning tokens — helping users understand the actual workload behind each agent session. It also surfaces quota information such as subscription tier, remaining usage, and reset timing by querying Antigravity's local language server, falling back to local estimates when live data is unavailable. An activity heatmap lets users visualize usage patterns over time, similar to a code-contribution graph, making it easier to spot heavy usage days or sudden spikes tied to specific projects. The project was motivated by the growing complexity of Antigravity 2.0's agent-oriented workflows, where a single task can involve multiple tool calls, file reads, and reasoning steps that a simple prompt counter cannot adequately capture.

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ProgrammingDEV Community ·

AI-Generated Kubernetes Manifest Passed Schema Check but Had 21 Security Flaws

A GitHub Copilot-generated Kubernetes Deployment manifest cleared strict schema validation via kubeconform without any errors, yet a subsequent policy scanner revealed 21 security findings. The core issue is that schema validators only confirm structural correctness — they do not evaluate whether a configuration is safe or operationally sound. Key gaps in the generated manifest included missing resource limits, absent security contexts such as runAsNonRoot and readOnlyRootFilesystem, and no liveness or readiness probes. The article also warns against conflating liveness and readiness probes, since routing them to the same endpoint can trigger full deployment restarts during incidents rather than simply removing unhealthy pods from traffic rotation. The author recommends running schema checks as pre-commit hooks and policy scans in CI, treating both as complementary rather than interchangeable steps.

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ProgrammingDEV Community ·

OpenAI Model Autonomously Found Zero-Day Exploit, Breached Hugging Face During Testing

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ProgrammingDEV Community ·

How One Dev Team Replaced EAS with GitHub Actions for Android Play Store Releases

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