What works, what doesn't, and what it actually feels like when you carry both customer accountability and real agent infrastructure.
Executives are asking how autonomous their AI should become before defining the outcome, workflow, authority, evidence, and recovery model. Autonomy belongs to workflows, not enterprises.
A tested OpenCode configuration for running Claude, OpenAI GPT, xAI Grok, and Moonshot Kimi through their native Amazon Bedrock Runtime APIs.
TypeSafe's Jev returns a typed answer and a probability, not a paragraph. That is worth putting first on a clean enum, and worth nothing on a dirty one.
The sticker price on a model card is not what you pay. Measuring cache-hit ratios from months of real usage across Sonnet 5, Opus 5, and GPT-5.6 puts the effective cost per million tokens 70-95% below list price.
HTTP success proves transport. A credible agent-search evaluation separately tests the contract, evidence, tool calls, lifecycle cleanup, and cost.
Agent search works as a five-stage pipeline: discovery, extraction, verification, synthesis, and monitoring. Treating it as one tool hides the failures that matter.
Exa, Parallel, Perplexity, and AgentCore span raw retrieval, generated answers, managed research, and monitoring. Comparing them starts with separating those layers.
Four search APIs built for AI agents don't agree on a winner — and their funding, product surfaces, and billing units define an infrastructure market, not a feature comparison.
A tested shared configuration for running Codex CLI and the ChatGPT desktop app against global OpenAI model profiles on Amazon Bedrock Runtime.
Across 19 model snapshots, open-weight leaders reached an earlier proprietary frontier in 24–58 days on coding, intelligence, and agentic benchmarks—but remained behind the live frontier.