gonna read this with a grain of salt because I have been rather unimpressed with Google's Ai products, save direct API calls to gemini

The rest is trash they are forcing down our throats

This is a neat idea but there are so many variables here that it's hard to make generalizations.

Empirically, a top level orchestrator that calls out to a planning committee, then generates a task-dag from the plan which gets orchestrated in parallel where possible is the thing I've seen put in the best results in various heterogeneous environments. As models evolve, crosstalk may become less of a liability.

I’ve been building a lot of agent workflows at my day job. Something that I’ve found a lot of success with when deciding on an orchestration strategy is to ask the agent what they recommend as part of the planning for phase. This technique of using the agent to help you improve its performance has been a game changer for me in leveraging this tech effectively. YMMV of course. I mostly use Claude code so who knows with the others.

The "alignment principle" vs "sequential penalty" finding mirrors my production experience exactly.

I run a multi-agent system where specialized agents handle different business functions (customer support, code review, deployment monitoring). The key insight: task decomposability determines architecture.

Parallelizable tasks (analyzing independent customer tickets, running separate test suites) show massive gains with independent agents. Sequential workflows (debugging a specific issue that requires following a chain of logic) degrade with coordination overhead.

The "tool-use bottleneck" is real. We hit it around 12-15 tools per agent. The coordination tax becomes severe. Solution: role-based tool access. Support agents get 5 tools, deployment agents get 8, code review agents get 6. Overlap is minimal.

One counter-intuitive finding: persistent memory per agent beats centralized knowledge. Each agent has AGENTS.md (instructions), TOOLS.md (available actions), and memory/ directory (session logs). Agents learn from their own mistakes without polluting each other's context.

The error amplification metric (17.2x for independent vs 4.4x for centralized) explains why we use a hub-and-spoke model with human checkpoints at handoff boundaries.

Documented these patterns at howtoopenclawfordummies.com for anyone building similar systems.