A developer has released an open-source enhancement to Claude's dynamic workflows concept, allowing teams to orchestrate multiple AI agents and models in a single workflow. The tool, called awman, lets users configure which agents and models to use, set rules for how the workflow should be designed, and designate a leader agent to automatically create a custom workflow—addressing practical needs like reducing model bias, spreading usage limits across subscriptions, and mixing remote and local models.
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A developer has added a `--dynamic` flag to awman, an agent workflow manager they've been building since the beginning of the year, enabling dynamic workflows that can combine multiple agents and models instead of being locked to a single LLM.
Why it matters
The tool addresses three practical constraints: reducing bias by running the same problem across different AI models, distributing usage across multiple subscriptions to avoid hitting rate limits on a single provider, and supporting both remote and local models in a single workflow.
What to watch
The system works by designating a leader agent that designs a custom workflow (stored as a TOML file) based on a configured list of available agents/models and a set of rules passed to it—all open-source, meaning developers can adapt it to their own harnesses and models rather than relying on Claude alone.
The developer began by wanting to replicate Claude's dynamic workflows concept—where an AI system designs a workflow tailored to a specific task—but needed the ability to mix multiple agents and models rather than remain locked into a single provider. They identified three concrete motivations: first, different models have different blindspots and biases, so running the same problem through several agents could yield more robust answers; second, single-provider usage limits are a bottleneck in production systems, so distributing requests across multiple subscriptions would increase throughput; and third, they wanted to leverage both remote models (cloud APIs) and local models (via frameworks like OpenCode and oMLX) in the same workflow. To implement this, the developer added a `--dynamic` flag to awman's `exec workflow` subcommand. The mechanism requires three pieces of configuration: a list of agents and models that the workflow is permitted to use, a set of guidance rules that define constraints or preferences for how the workflow should be structured, and the selection of a leader agent. When the flag is invoked, awman launches the leader agent, which uses the guidance rules to design a custom workflow—output as a TOML file—that orchestrates the available agents and models. This architecture decouples workflow design from any single provider, making it portable across different model harnesses and allowing teams to mix their own combination of tools.
The post describes a response to a limitation of Claude's existing dynamic workflows feature: while Claude introduced the concept, it ties users to Claude's own models and infrastructure. The developer's approach decouples the workflow-design logic from any specific provider, allowing a leader agent (which could be Claude or another model) to orchestrate a team of heterogeneous models—some hosted, some local—according to rules the user defines. This is particularly valuable in environments where no single LLM subscription meets all requirements, or where redundancy and bias reduction across models matter more than consistency. By open-sourcing the mechanism and making it model-agnostic, the developer enables other teams to adapt the pattern to their own constraints and tooling.
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