this is such a trivial thing to do in DSPY, no one bothered to give it a name…
here's 7 lines
import os
import dspy
lm = dspy.LM("openrouter/z-ai/glm-5.3-flash", api_key=os.environ["OPENROUTER_API_KEY"])
jev = dspy.Predict('email:str -> choice:Literal["Legitimate", "Spam", "Phishing"]')
email = "Payroll asks for your password on a non-company sign-in page."
pred = jev(email=email, lm=lm)
print(pred.choice)
there are other options, obviously. you can choose to give it some tools, maybe some reasoning stage before picking a choice, and that's on top of the "reasoning" the llm model already does api side
Tenuo guarantees the agent's actions stay within the defined boundaries but it doesn't influence what the agent "thinks" about or what it decides to do within those boundaries
The task bound idea is interesting. I’m starting to hear more of this theoretically on AI enterprise architecture. Has anyone actually implemented it successfully?
Interesting to experiment with. As AI agents get faster and cheaper (and thus more accessible) having good governance around their actions becomes more important to cut down on shadow agents/runaway agentic actions and consequences
here's 7 lines
there are other options, obviously. you can choose to give it some tools, maybe some reasoning stage before picking a choice, and that's on top of the "reasoning" the llm model already does api side