One Prompt Line Leaks Unsupported RAG Claims in 40% of Cases

Strip the framing from both reports and the designs converge. A single orchestrator owning full context, spawning isolated short-lived sub-agents that each return one summary, beat single-agent by roughly 90% on specific research tasks at roughly 15x the token cost. Cognition's rejected pattern was peer sub-agents making independent, colliding decisions. That is a different topology, not a different verdict. The variable under test is orchestration structure, and the 15x/90x tradeoff is a hypothesis to rerun on your own workload. The thing the headline number doesn't tell you is whether your tasks look anything like theirs.

The binding constraint is arithmetic, and it is unforgiving. At 95% per-step accuracy, 20 sequential steps yield roughly 36% end-to-end success. Intuition does not survive contact with that exponent. Any eval harness for a chain longer than about 5 steps needs to plot empirical step-level accuracy against the 0.95^n curve, not just report a pass rate. Anthropic's own case data supports the discipline rather than the magic: a roughly 1M-line Zig-to-Rust port finished in under two weeks, and not through one-shot generation. It ran on strong judge/test gating as the trust mechanism. That gating is the reproducible part of the claim.

The longer-horizon signal reorders memory priorities. Across 12 agent memory systems, reliability under fact-updates and cross-session reasoning is set at write time (extraction/schema), not query time. Graph-based typed memory wins on correctness, but it can cost orders of magnitude more latency per query, and the win only holds when updates stay local. Flat/append-only stores fail for a mundane reason: schema-less extraction fills the graph with generic nodes that no later query can disambiguate. That structure cannot be recovered without a rebuild.

The extraction schema is largely irreversible, so multi-agent is a token tax that only pays off when an orchestrator owns the context and the write-time structure is right before volume hits.

The production read is straightforward. Externalize agent state to serializable storage, put hard escape hatches on every loop, and treat the memory-architecture decision as a one-way door. The token tax is measurable. The rebuild cost is not, until it arrives.

The single-A10G vLLM benchmark makes the tradeoff concrete. The config delivering 50% higher token throughput pushed p95 time-to-first-token to 494ms, against 50ms for the latency-optimized config. That is close to a 10x regression on the number users actually feel. The metric worth watching is goodput: the share of requests meeting both throughput and TTFT/TPOT targets. It shifts by workload. Chatbot, reasoning, and agentic traffic each want a different config, and getting there is a same-week instrumentation change, not a re-architecture.

One layer up, the vendor-number trap has the same shape. Poolside's Laguna S 2.1 (118B total / 8B active MoE) posts 109 tok/s under vLLM/NVFP4 at 256k context, edging Qwen3.5-122B's 103. The thing the leaderboard number doesn't tell you is what a real deployment does: a Q4_K_M setup on a 5-GPU/96GB rig starts at 40 tok/s and degrades to ~20 as context fills. Same model, a 2-5x throughput spread on serving config alone. Grounding is the worse story. Laguna produced 3 confirmed fabrications in 125 runs against Qwen's zero, cut to 1 only after a tokenizer/template fix plus 0.7 temperature / 0.95 top-p sampling. The out-of-box GGUF config actively degrades accuracy.

Prompt-cache economics quietly compound both problems. Measured TTLs, as opposed to documented ones, run Anthropic ~5 min, OpenAI 5-10 min, with Gemini's implicit cache more variable. Any cost model for agentic or batch workloads with idle gaps in that range rests on assumptions, not SLAs.

The throughput gain is real. It is also the wrong optimization target. p95 latency and grounding accuracy are the numbers users and the legal team actually experience.

Both benchmarks are single-source and unreplicated. The A10G result is one GPU at one config point and says nothing about H100s. Given that, I would expect the direction of these results to hold and the magnitudes to move. Every number here needs re-running on the stack it will actually serve.