Example: instead of telling your agent: “Open Google Calendar, create a new event, invite Mark, set it for 3 PM,” you say: “Set up a meeting with Mark tomorrow before 3 PM, but only if he has questions about the report I sent him.” This requires Generative AI combined with planning algorithms.
- Decide what to do next
Example: a user asks your chatbot a question it doesn’t know the answer to and instead of immediately escalating to support, the agent decides: Should I ask a follow-up question? Search internal docs? Try the web? Or escalate now? This step needs decision-making capabilities via reinforcement learning.
- Handle unexpected scenarios
Example: an agent tries to schedule a meeting but one person’s calendar is blocked. Instead of failing, it checks for nearby open slots, suggests rescheduling, or asks if another participant can attend on their behalf. True agents need reasoning or probabilistic thinking to deal with uncertainty. This might involve Bayesian networks, graph-based logic, or LLMs.
- Learn and adapt based on context
Example: you create a sales assistant agent that helps write outreach emails. At first, it uses a generic template. But over time, it notices that short, casual messages get better response rates, so it starts writing shorter emails, adjusting tone, and even choosing subject lines that worked best before. This is where machine learning, especially deep learning, comes in.
- Understand goals, not just fixed instructions
Example: instead of telling your agent: “Open Google Calendar, create a new event, invite Mark, set it for 3 PM,” you say: “Set up a meeting with Mark tomorrow before 3 PM, but only if he has questions about the report I sent him.” This requires Generative AI combined with planning algorithms.
- Decide what to do next
Example: a user asks your chatbot a question it doesn’t know the answer to and instead of immediately escalating to support, the agent decides: Should I ask a follow-up question? Search internal docs? Try the web? Or escalate now? This step needs decision-making capabilities via reinforcement learning.
- Handle unexpected scenarios
Example: an agent tries to schedule a meeting but one person’s calendar is blocked. Instead of failing, it checks for nearby open slots, suggests rescheduling, or asks if another participant can attend on their behalf. True agents need reasoning or probabilistic thinking to deal with uncertainty. This might involve Bayesian networks, graph-based logic, or LLMs.
- Learn and adapt based on context
Example: you create a sales assistant agent that helps write outreach emails. At first, it uses a generic template. But over time, it notices that short, casual messages get better response rates, so it starts writing shorter emails, adjusting tone, and even choosing subject lines that worked best before. This is where machine learning, especially deep learning, comes in.