TL;DR
- AI agents are systems that act autonomously in digital or physical environments.
- They integrate tools, memory, and planning to achieve meaningful goals.
- Effective AI agents follow design principles like goal alignment, inclusivity, transparency, security, and governance.
AI agents are systems that can act autonomously in digital or physical environments. They differ from traditional software by being goal-driven and capable of decision-making. This blog post can explore the definition, capabilities, tools, memory, planning, design principles, use case selection, and value-backwards strategy used by Fractal with AI agents. The video embedded at the end of the post provides further insights into these concepts.
What are AI Agents?
Definitions of AI agents vary significantly depending to who you ask, including among the top LLMs provided in the industry: OpenAI, Google, or Anthropic, to name a few. A working definition we use is that AI agents are programs capable of acting in an environment to fulfill a goal. They are designed to be goal-driven and capable of decision-making, setting them apart from traditional software.
AI agents vs. LLMs
While large language models (LLMs) generate language and perform reasoning, they lack memory and action capabilities. AI agents use LLMs but also integrate tools, memory, and planning to act meaningfully. This integration allows AI agents to achieve goals that LLMs alone cannot.
Tools vs. Agents
The tools agents may leverage are broad and include calculators, APIs, ML models, and LLM system prompts. Agents orchestrate tools to achieve goals, making them more than just smart tools. AI agents leverage these tools to perform complex tasks and make decisions autonomously.
Typically, agents will interact with tools through open protocols such as MCP (learn more about MCP in this earlier post) or A2A (learn more about A2A here) when interacting with other agents.
Memory and Planning
AI agents utilize different types of memory:
- Short-term: conversation context
- Long-term: user preferences
- Persistent: system goals
Planning enables agents to reflect, course-correct, and align with goals.
This combination of memory and planning allows AI agents to adapt and improve over time.
Design Principles
Effective AI agents follow several design principles:
- Goal Alignment: Maximize long-term value, not just short-term gains.
- Inclusivity: Understand diverse users and contexts.
- Transparency: Clearly disclose AI involvement.
- Security: Validate inputs and protect data.
- Governance: Start small, iterate, and allow human oversight.
These principles ensure that AI agents are designed to be effective, ethical, and secure.
Use Case Selection
Decisions range from operational (automated by agents) to strategic (human-led). We’re now seeing the industry focus shifting toward high-value, low-risk applications where AI agents are being used in various industries to automate tasks and improve efficiency.
Value-Backwards Strategy
At Fractal, we believe that the best approach to design is to start with business value (speed, cost, revenue, quality), then design the AI solution.
We use a structured framework to develop both Proof of Concepts and to deploy them at scale: value mapping → data → AI output → tech → adoption → governance.
This strategy ensures that AI agents are designed to deliver maximum business value.
Conclusion
AI agents represent a significant advancement in technology, offering capabilities beyond traditional software and LLMs. By integrating tools, memory, and planning, they can achieve meaningful goals and drive substantial business value.
The video embedded below provides further insights into these concepts.
