AI Development78 articles · Page 3 of 7
AI Development
Implementation notes for building AI tools around real business data, handoffs, review queues, and safeguards.
AI Development articles, page 3
AI Development
System prompts are not an agent control plane
AI agents need enforcement points before risky tool calls run. System prompts can guide behavior, but refunds, emails, account deletion, and customer work need runtime policy, approvals, logs, and receipts.
8 min read
AI Development
The fast-food chatbot problem is a guardrail problem
Viral fast-food chatbot screenshots are funny because the failure is ordinary: the bot is supposed to help with lunch, but the model underneath still wants to be a general assistant.
7 min read
AI Development
Copilot cohorts make AI adoption a management problem, not a seat-count problem
GitHub's new Copilot cohort metrics give leaders a better way to ask whether AI is changing delivery work, not just whether licenses are enabled.
8 min read
AI Development
Agent receipts: what to log before AI touches customer work
Before an AI agent sends a message, updates a record, publishes a page, or changes a CRM note, the team needs a receipt that shows what happened, why, who reviewed it, and how to roll it back.
7 min read
AI Development
GitHub's Accessibility Agent Worked Because the Mess Was Already Organized
GitHub's experimental accessibility agent shows the real prerequisite for useful accessibility automation: structured issues, WCAG metadata, acceptance criteria, and human review habits.
7 min read
AI Development
ITBench-AA shows why enterprise IT agents need receipts before root access
ITBench-AA shows a familiar enterprise AI failure mode: agents can investigate Kubernetes incidents plausibly, then confuse symptoms for root causes. Before teams let agents touch infrastructure or workflows, they need receipts, scope, approvals, escalation, and replayable evals.
8 min read
AI Development
Your AI dashboard needs a quality lane, not just GPU charts
A green inference dashboard can still miss the failure that matters: the model is fast, available, and wrong. Production AI teams need to monitor both infrastructure quantity and output quality.
7 min read
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
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