What Can Agentic AI Do?
What is Agentic AI?
Agentic AI refers to AI systems that can act with autonomy, problem-solve, and react to different situations. This type of AI can interact with the real world by taking actions, observing their effects, and adapting responses accordingly. It involves AI that can make decisions and carry out tasks with a degree of independence. Think of it as AI capable of: executing tasks autonomously, responding to new scenarios, interacting with other systems or humans.
An AI agent is a software program that can interact with its environment, gather data, and use that data to achieve specified or evolving goals. AI agents can choose actions to optimize the outcomes for those goals. Key characteristics include:
- Autonomy: The agent can perform actions without constant human intervention, though a human can remain in the loop for oversight.
- Memory and knowledge: The agent can store information about its environment, decisions, or user preferences. Some agents integrate AI models, such as LLMs, for information processing and decision-making.
- Perception: The agent can perceive and process information from its environment to guide decisions.
- Tool usage: Agents may use tools such as APIs, code interpreters, or internet resources to perform tasks.
- Collaboration: Agents can interact and collaborate with humans or other agents to accomplish tasks more efficiently.
Multiple types of AI agents exist, including learning agents, simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. Systems using AI agents can be designed using different architectures:
- Single-agent: Serves as a personal assistant or performs tasks independently.
- Multi-agent: Multiple agents interact collaboratively or competitively.
- Human-machine: Agents interact with humans to enhance task execution efficiency.
Automation vs AI Workflow vs AI Agent
| Automation | AI Workflow | AI Agent | |
|---|---|---|---|
| Definition | A program that executes predefined, rule-based tasks automatically | A program that calls an LLM via API for one or more steps | A program designed to perform non-deterministic tasks autonomously |
| Core foundations | Boolean logic | Boolean logic, Fuzzy logic | Fuzzy logic, Autonomy |
| Tasks | Deterministic, predefined tasks | Deterministic tasks requiring flexibility | Non-deterministic, adaptive tasks |
| Strengths | Delivers reliable outcomes, fast to execute | Better handling of complex rules, great for pattern recognition | Highly adaptive to new variables, simulates human-like behavior and reasoning |
| Weaknesses | Limited to tasks explicitly programmed, cannot adapt to new scenarios, struggles with complexity | Requires data to train models effectively, harder to debug and interpret | Less reliable, may produce unpredictable undesired outcomes, slower to execute |
| Example | Send a Slack notification every time a new lead signs up on our website | Analyse, score and route every website inbound lead using ChatGPT | Perform a full internet search on every inbound lead and update info |
Know Your AI Agent Protocols!
As Aristotle implied: purpose governs action—without it, intelligence collapses into efficient error. Modern framework priorities are: Context (what to know) → Intent (what to achieve) → Harness (how to execute) → Feedback. Autonomy usually fails not from weak execution, but misordered thought. Legitimation & adversarial modeling are true enablers of viable autonomy, averting governance collapse from intent neglect.
MCP, A2A, ACP, ANP—AI agent building blocks:
- ✅ RAG → better answers (retrieval + grounded generation)
- ✅ MCP → context-aware models
- ✅ A2A → multiple agents collaborate
- ✅ ACP → structured agent communication
- ✅ ANP → scalable agent networks
Layer them right → context + action + coordination.
