Below are four working patterns we have seen in production systems:
1️⃣ Adaptive feedback loops
Agents perform tasks → Supervisor evaluates → Reward service updates policies → Guidelines are adjusted → Agents improve over time.
A continuous learning cycle is created, where the system reinforces effective behavior and reduces risky behavior. This is reward-based learning, which improves with each iteration.
2️⃣ Corrective action
A centralized Supervisor distributes tasks, compares results with the application's guidelines, and connects alternative agents if errors are detected. The best verified result is returned to the user. This prevents a bad result from reaching end users.
3️⃣ Human in the loop
For sensitive domains (medicine, law, finance), agents generate preliminary responses, but a human validates them before execution. The flow is automatically paused for expert review and resumes only after approval.
4️⃣ Emergency stop
Critical for high-risk systems, such as trading.
Agent 1 collects market data → LLM processes signals → Agent 2 evaluates conditions → if anomalies or risks are detected, execution is immediately stopped.
Example: a trading bot with access to a volatility API showing VIX = 42 (extreme market stress). Even if the bot suggests an aggressive trade, the evaluator independently checks: "Is this adequate given the current volatility?" If not - the action is completely blocked.
The underlying philosophy is behavior shaping: a three-step loop of evaluation → feedback → correction. The evaluator doesn't just record the result post-factum. He actively intervenes: rolls back bad transactions, stops flows with incorrect data, or redirects complex cases for human verification.
It's especially important when agents interact with unstable external states - market conditions, API health, system load. The evaluator provides a sanity-check to ensure that the model correctly interpreted the signals and didn't just generate coherent text.
The goal is not to catch all errors in advance (this is impossible). The goal is to build systems that detect problems on the fly, understand what went wrong, and automatically correct the course before the damage spreads.
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