AI Development94 articles · Page 5 of 8
AI Development
Implementation notes for building AI tools around real business data, handoffs, review queues, and safeguards.
AI Development articles, page 5
AI Development
Codex is moving AI coding agents into the customer feedback loop
Braintrust and Endava show a more useful pattern for AI coding agents: faster movement from customer request to preview branch, working spec, sandbox run, or reviewable delivery artifact.
8 min read
AI Development
OpenAI's eval playbook makes the harness part of the result
A 92% success rate is not enough to approve an AI agent pilot. Teams need to know what tools, retries, prompts, budgets, safeguards, and receipts produced the score.
8 min read
AI Development
Your AI Agent Needs a Bug Cemetery, Not Another Demo
AWS Bedrock AgentCore datasets point to a practical habit for reliable agents: turn production failures into versioned regression tests with locked inputs, expected tool calls, assertions, and CI gates.
7 min read
AI Development
Enterprise IT agents just got a harder benchmark. The best models still missed half the incidents.
ITBench-AA shows why enterprise IT agents need scoped pilots, workflow receipts, eval datasets, approval gates, and human escalation before they touch production systems.
8 min read
AI Development
Google's Agent Executor shows why AI agents need runtime infrastructure
Google's Agent Executor points to a practical shift: production AI agents need durable execution, isolation, state consistency, recovery, and audit trails.
7 min read
AI Development
OpenAI's tax agents show why AI automation needs a feedback loop
OpenAI's Tax AI pilot with Codex is less a story about automated tax prep and more a lesson in production AI: agents improve when practitioner corrections become structured evidence, evals, and guarded releases.
8 min read
AI Development
Microsoft's Computer-Using Agents Are GA. The Real Story Is Legacy Workflow Automation.
Microsoft's Copilot Studio computer-using agents make AI-driven UI automation generally available. For SMB teams, the opportunity is not letting agents roam across screens. It is using governed workflows to bridge legacy systems that lack usable APIs.
8 min read
AI Development
Mistral Workflows shows what production AI automation actually needs
Mistral Workflows is not just another agent builder. It points to the operational checklist every SMB team should use before moving AI workflows from prototype to production.
7 min read
AI Development
GitHub's Copilot app turns coding agents into delivery sessions
GitHub's latest Copilot updates show AI coding agents moving beyond chat and into the software delivery loop: isolated sessions, pull request context, validation, review comments, failing-check fixes, and conditional merges.
11 min read
AI Development
Agent evals should test workflow receipts, not just model answers
If an AI agent is supposed to do work, the eval should inspect the receipt of that work: source data, tool calls, approvals, state changes, and recovery behavior.
7 min read
AI Development
The agent metric that matters: governance gets 12x more AI projects into production
Databricks' 2026 State of AI Agents report points to a practical lesson: governance and evaluations are becoming deployment infrastructure.
5 min read
AI Development
Google's Managed Agents make hosted AI sandboxes a business planning issue
Google's Managed Agents in the Gemini API show how hosted AI agent sandboxes are becoming part of business automation planning, not just developer experimentation.
8 min read
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