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 gather6d ago · 6 min readblog.aiperceivable.com
apexe: Run Your Tools. Enforce Your Rules.Your team already has tools that work: command-line utilities, internal CLIs, operational scripts, and HTTP APIs. They encode years of decisions about how your systems should behave. As AI agents begiOct 16 · 8 min readblog.aiperceivable.com
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 finishinMay 3 · 4 min readblog.aiperceivable.com
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 billingApr 30 · 6 min readblog.aiperceivable.com
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-perApr 2 · 4 min readblog.aiperceivable.com
