Post by Sandwich Lab

36% CTR. Zero conversions. An agent deployed Arabic-language creatives in #SouthAfrica. Upstream click data looked strong. The system was ready to scale. But downstream data told a different story: not a single effective conversion. It was a false optimum, amplified by local proxy metrics. This is the core problem with how most agent systems handle enterprise growth today. Current systems solve it by keeping humans in the loop. #CursorTab exposes every intermediate step for human approval. #ClaudeCode lets humans review the plan before the agent executes. Both approaches work within their limits. But neither can run 24/7 at enterprise scale. The more you delegate, the more the human review bottleneck compounds. The real question is: what happens when you remove the human from the loop? The answer requires a system that can review itself. Review Mode for Agent is built on four components: 1.Trajectory logging and versioning Every execution is recorded in full, including which algorithm variant produced which outcome. Without this, there is nothing to review. 2.Evaluation Each run maps to a comparable performance signal. This is what separates a log from a learning system. 3.Dynamic benchmark maintenance The evaluation surface must evolve. The #SouthAfrica case is a precise example: a benchmark that only tracked upstream CTR would have reinforced a strategy that produced no business value. The system needs to continuously retire outdated benchmarks and turn newly discovered failure patterns into new test constraints. 4.Skill and policy update Experience must convert into future capability. Not as static rules, but as continuously updated strategy structures that can be recompiled across different channel configurations. Advertising is a noisy sequential decision problem with delayed feedback, multiple constraints, and a changing environment. Static rules and one-shot tuning are not sufficient for long-term optimization. Lanbow models it as a long-running, experiment-driven optimization process. Humans define the objective and the operating boundary. The agent runs continuously within that boundary, proposes strategy experiments, evaluates results, and updates its own skills. The full technical write-up is in the comments. #Lanbow #EnterpriseGrowth #DecisionSystem #AgenticAI #SandwichLab