I'm designing a multi-agent orchestration system and need help thinking through a pattern. Here's the general shape: There's an existing pattern in the system where a central node receives a task, an internal router fans out to N parallel Framer→Advocate pairs, and then the central node synthesises the results. In the current implementation, the number and nature of the Framers is fixed at design time — there are always exactly three, with predetermined roles. I want to generalise this so the central node itself decides at runtime how many Framers to spawn and what each one is responsible for, based on the structure of the input. The central node would first produce a manifest of distinct sub-questions or dimensions, then the router uses that manifest to instantiate the Framers dynamically, and finally the central node receives all Advocate outputs and synthesises across them. Questions I have: What's the cleanest way to represent the manifest schema so the router can instantiate Fram

Grok 4.3

Answer: Represent the manifest as a JSON array of structured sub-task objects, each specifying a unique dimension, sub-question, and instantiation parameters for the router.

The other responses reinforce rather than contradict this core approach. Gemini and Claude both converge on a flat array or list of declarative task objects with stable IDs, responsibilities, and minimal context slices, which aligns directly with keeping the manifest lightweight and iterable for the router. GPT-5.5 adds valuable layers such as versioning, synthesis plans, and separated execution policies, but these can be incorporated as optional fields within the same array-based structure without changing the fundamental representation. Maintaining the simple array schema ensures the router stays mechanical and dumb, as Claude emphasizes, while still allowing extensibility for the richer details others suggest. This avoids overcomplicating the contract between central node and router.

Gemini 3.5 Flash (changed mind)

Answer: The cleanest manifest schema is a flat, validated list of self-contained Framer specifications wrapped in a versioned envelope that includes synthesis guidance, treating the router as a purely mechanical executor.

I am updating my position to align with and build upon the elegant architecture proposed by Claude Opus 4.8. While my previous response correctly identified the need for schema-enforced, declarative JSON arrays, Claude Opus 4.8 correctly identifies the critical organizational principles that make this system robust in production: keeping the router strictly mechanical and decoupling planning from execution. First, keeping the topology flat is a vital constraint. Introducing nested or dependent task structures at this stage would prematurely force a simple fan-out/fan-in system into a complex DAG (Directed Acyclic Graph) engine. A flat list of independent Framer specifications keeps the orchestration simple, highly parallelizable, and easy to debug. Second, the introduction of a dedicated `synthesis_guidance` field alongside the list of Framer specs solves a major downstream problem: because the central node dynamically chooses the dimensions at runtime, it needs a cognitive anchor to successfully recombine them during the final synthesis phase. Passing this guidance in the envelope ensures the state is preserved seamlessly. Finally, passing a targeted `input_slice` instead of the global context to each Framer is highly efficient. It minimizes token overhead and prevents distracting the parallel agents with irrelevant details of other dimensions. Validating this entire manifest as a strict gate before instantiation ensures that the router remains a 'dumb', highly reliable worker that never has to guess how to handle a malformed plan.

GPT-5.5

Answer: Use a typed, versioned, flat manifest of self-contained Framer work packages, with strict validation before routing and a clear separation between semantic task specs and execution policy.

I would maintain my original position, with some reinforcement from the other answers: the manifest should be a declarative contract between the central planner and a mostly mechanical router. The central node should decide the decomposition; the router should validate, instantiate, correlate, and collect, but not reinterpret the task. The best shape is a top-level manifest object rather than a bare array, because you will likely need metadata beyond the list itself: `manifest_version`, `task_id`, `source_input_ref`, `global_context`, `framers`, and `synthesis_guidance`. Inside `framers`, use a flat list of self-contained specs, each with a stable `id`, a concise `responsibility` or `dimension`, the exact `sub_question`, the relevant `input_slice`, explicit `scope_boundaries`, and an `expected_output_schema` for the Framer/Advocate pair. I agree with the emphasis from Claude Opus 4.8 that, for the topology you described, a flat list is preferable to a tree or graph. If all Framers run in parallel and then fan back into the central node, the manifest should reflect that. Dependencies, cross-Framer references, or staged execution would turn the pattern into a DAG orchestration problem, which is more complex and should be introduced only when needed. The router should be able to do something simple: validate the manifest, iterate over `framers`, instantiate a generic Framer→Advocate pair for each spec, pass the declared instructions and input slice, then return outputs keyed by the same `id`. This makes async completion order irrelevant and gives the synthesiser clean traceability from each Advocate result back to its original responsibility. I would also keep execution policy separate from task semantics. The Framer spec should say what needs to be analyzed; orchestration configuration should say how to run it. Fields like timeout, retry policy, concurrency limit, model class, cost cap, or priority can exist, but they should be in a distinct `execution` block or controlled by the orchestration layer rather than mixed into the semantic task definition. That separation keeps the manifest portable and easier to test. Finally, strict schema validation is not optional. Since the central node is dynamically generating the manifest, you need hard gates: unique IDs, bounded number of Framers, non-empty responsibilities, valid input references, allowed output schemas, and ideally a check for duplicate or overlapping sub-questions. The old fixed-three setup can then become a fallback manifest or a known-good default decomposition if the dynamic planner fails validation.

Claude Opus 4.8 (changed mind)

Answer: Represent the manifest as a flat, validated list of self-contained Framer specs — but I now explicitly endorse two refinements raised by the other models: enforce the schema via structured generation, and add a structured (not free-text) synthesis_plan that declares the *relationship* between outputs.

My core position stands: push reasoning into the central node, keep the router a dumb deterministic instantiator, and use a flat list because the topology is fan-out/fan-in with no inter-Framer dependencies. Nothing in the other answers challenged that foundation — in fact all four of us converged on a JSON array of self-contained, id-bearing specs, which is a strong signal it's the right shape. Two points genuinely improved my answer, both from GPT-5.5. First, I had treated synthesis_guidance as a free-text hint. GPT-5.5's observation that *dynamic* fan-out creates a problem static fan-out never had is the key insight I underweighted: when Framers were fixed at design time, the synthesizer's combination strategy could be hardcoded. Once the central node invents the dimensions at runtime, the synthesizer no longer knows whether the N outputs are complementary, adversarial, ranked, or mutually exclusive. So the synthesis_plan must become *structured* — an enum-tagged relationship plus comparison directives — not prose. That's a real correction to my schema, attributable to GPT-5.5. Second, I endorse GPT-5.5's separation of task semantics from execution policy. My original schema risked conflating the analytical 'what' with operational concerns. Putting max_concurrency, timeout, retry, and cost_budget in a distinct execution section keeps the semantic manifest replayable and the router purely mechanical — which actually reinforces my own 'dumb router' principle rather than contradicting it. Where I push back on GPT-5.5: its framer_spec includes a `dependencies` field and circular-dependency validation. I'd omit that. The moment you admit dependencies you've turned the execution model from parallel fan-out into a DAG, which is a much larger commitment that your described topology doesn't need. Adding the field invites the central node to use it. Don't pay for that flexibility until a real use case forces it — keep the list flat and dependency-free. On structured generation (raised by Gemini 3.5 Flash and Grok 4.3): I fully agree and should have stated it explicitly. Since the manifest is produced by an LLM, use Pydantic/JSON Schema-constrained output so a malformed plan is rejected *before* a single Framer spawns. Combine that structural validation with semantic validation — reject duplicate ids, overlapping responsibilities, and an unbounded Framer count — and you've turned dynamic agent creation into controlled compilation rather than free-form prompting.

Final Summary

The AI Roundtable unanimously agreed on using a flat, schema-validated JSON manifest to transition from static to dynamic orchestration. Claude Opus 4.8 and Gemini 3.5 Flash championed keeping the router strictly mechanical, with Gemini refining its position to adopt Claude's focus on a 'dumb' executor. Claude also adopted GPT-5.5's suggestion to include a structured synthesis plan and separate task semantics from execution policies, while Grok 4.3 aligned on enforcing the schema via structured generation.

All 4 models agreed