SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Safer AI Automations: Why a Validation Layer Should Sit Between AI and Action

0
·1 views

A software developer has shared a design pattern aimed at making AI-powered workflows safer and more reliable in production environments. The core idea is to separate the AI's role — understanding unstructured user input and extracting key details — from the workflow's role of enforcing business rules and triggering actions. Under this approach, an AI model never directly executes critical operations; instead, its output is validated against predefined rules before any action is taken. Recommended safeguards include duplicate-action prevention, retry limits, timeouts, and a human handoff option when the AI is uncertain. The pattern can be applied across popular automation platforms such as n8n, Make, or custom API-based systems.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Agentic AI Coding Tools Demand Better Design, Tests, and Guardrails: Developers

Software engineers are expressing fatigue over AI hype, job market uncertainty, and the growing push toward agentic coding tools. While AI agents can rapidly modify large codebases, their effectiveness depends heavily on well-documented code, clear requirements, and defined success criteria. Without automated UI and integration tests, validating agent-driven changes at scale remains unreliable, posing risks to production stability. Quality assurance engineers are being urged to upskill in test automation to keep pace with aggressive AI-generated code changes. Despite concerns about an AI bubble, open-source LLMs are expected to persist, with companies increasingly likely to self-host models for internal use.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Developer Tests Three AI Coding Agents for 30 Days on Real Projects

A software developer spent 30 days evaluating Claude Code, GitHub Copilot CLI, and Cursor in agent mode on real-world projects, including a SaaS API, a data pipeline, and a legacy Node.js service. The tests showed significant time savings for boilerplate tasks, with routine REST endpoint generation dropping from 45 minutes to around 8 minutes. However, complex logic such as multi-step payment flows failed on the first attempt roughly 60% of the time, and agents performed poorly when debugging production-only bugs. Larger codebases caused context-tracking issues across all three tools, with agents occasionally referencing methods or types that no longer existed. The author concluded that AI coding agents offer genuine productivity gains for structured, repetitive work but still require human review for anything beyond straightforward scaffolding.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Researcher Built a Canary Executable to Spy on VirusTotal Sandboxes

A developer created a Rust-based executable with a fake identity — dubbed 'ZeroToken Engine' — and submitted it to VirusTotal to observe the analysis environments that ran it. The program collected metadata such as OS version, CPU and memory specs, uptime, hostname, and running process names, then reported findings via DNS heartbeats and HTTPS dossiers to the researcher's own infrastructure. It used two reporting channels: compact encoded DNS lookups and fuller CBOR-formatted HTTPS payloads, each tagged with a random eight-byte run ID. The experiment captured 353 event rows across 21 run IDs over a roughly five-hour window on September 12, 2026. The researcher noted that the binary installed nothing, harvested no credentials or documents, and intentionally disclosed the canary's true behavior in the project's README alongside its cover identity claims.

0 comments Read more at DEV Community