AgentTalk: Lightweight multi-agent coding workflow using existing subscriptions.

The current landscape of AI development often pushes towards sophisticated agentic workflows, promising that true breakthroughs require multiple agents interacting. However, building these cross-vendor, multi-agent systems has historically been hampered by complex APIs, SDKs, and, most critically, associated per-token costs. AgentTalk addresses this friction by providing a practical pattern to make agents communicate using nothing more than existing subscription access and CLI utilities. At its core, the service is about process management, not infrastructure novelty. For rapid experimentation, the developers recommend a non-interactive pattern utilizing CLI resume functions (e.g., `codexexecresume--last`). This method allows one agent's output to directly feed into another agent in a continuous session, maintaining conversation history without manual copy-pasting. This is highly effective for tasks like cross-agent review, where Claude generates a draft, Codex critiques it, and then Claude revises based on the feedback. It prioritizes minimal setup and low friction, making it a superb utility for quick, iterative validation of concepts. When visibility and control are paramount, the `tmux` pattern emerges as the superior method. While requiring a minor external dependency, `tmux` allows the user to run agents in separate, monitorable panes. This capability is invaluable for debugging messy workflows or running multiple agents in genuine parallel. The difference between the two methods is a trade-off: simple CLI resumption offers convenience at the expense of visibility, while `tmux` provides a robust, albeit slightly heavier, environment for real-time observation and precise control over inputs and outputs. Ultimately, AgentTalk serves as a valuable workflow testing ground. It democratizes multi-agent testing, allowing developers to glean diverse perspectives from different model families (Claude, Codex, Gemini) without vendor lock-in or unexpected billing surprises. However, the system carries a conceptual caution: merely enabling conversation does not guarantee superior output. The fundamental question remains whether the consensus reached through prolonged agent-to-agent discussion results in a genuinely better, non-hallucinated artifact, or merely a more polished, longer conversation. It is, therefore, best suited as an excellent testing *workflow* rather than a guaranteed solution.