APFlow: Orchestration That AI Agents Can Discover, Invoke, and OperateA closer look at APFlow's architecture: durable Runs, explicit recovery, shared token budgets, and a common capability contract across MCP, A2A, REST, and CLI. Consider a research workflow that gather6 min read·blog.aiperceivable.comOct 25
apexe: Run Your Tools. Enforce Your Rules.Give humans and AI agents a shared execution layer for existing CLI tools and HTTP APIs, with typed contracts, configured access rules, and auditing.12 min read·blog.aiperceivable.comOct 16
Beyond the Scaffold: Why Your Agent Harness Needs a Standard KernelBy now, every developer building with Large Language Models (LLMs) has realized a hard truth: a raw LLM is just a token predictor. To transform it into a reliable, autonomous Agent capable of finishin4 min read·blog.aiperceivable.comMay 3
The apcore ManifestoA standard for software that AI agents can actually use. The cognitive tax Picture the agent you shipped last Friday. It has access to fifty tools — internal microservices, third-party APIs, a billing6 min read·blog.aiperceivable.comApr 30
The Polyglot AI: Achieving SDK Parity across Python, TypeScript, and RustIn the rapidly evolving landscape of Agentic AI, developers often face a "Language Tax." You might prototype an AI tool in Python, but your production web backend runs on TypeScript, and your high-per4 min read·blog.aiperceivable.comApr 2
