What makes a system an AI agent, how the plan–act–observe loop works, and the building blocks behind tools, memory and orchestration.
A working definition
An AI agent is a system where a language model decides which steps to take, and in what order, to reach a goal. It can call tools such as search, databases or APIs, look at the results, and choose what to do next until the task is complete.
The agent loop
User goal → reasoning and planning → tool selection → tool execution → observation → next action or final response. The loop repeats until the model decides it has enough to answer or a limit is reached. This pattern was described in research such as ReAct, which interleaves reasoning steps with actions.
Tools
A tool is a function the model can request, described by a name, a purpose and the parameters it accepts. The application executes the tool, not the model, and returns the result. Well-designed tools have narrow responsibilities, clear descriptions and predictable outputs, which makes the model far more likely to use them correctly.
Memory and state
Short-term memory is the conversation and tool results held in the context window. Long-term memory stores facts or preferences between sessions, often in a database or vector store. Keeping memory concise matters: an agent that carries every past step forward becomes slow, costly and more error-prone.
Orchestration patterns
A single agent with a few tools handles many tasks. A router can hand requests to specialised agents. An orchestrator can break a large task into subtasks and combine the results. Each added agent adds latency, cost and failure points, so start simple.
Connecting to systems
Agents are only as useful as the systems they can reach. Standards such as the Model Context Protocol (MCP) define a common way to expose tools and data to models, which reduces custom integration work. Access should follow the principle of least privilege.
Common failure modes
Looping on the same tool call, choosing the wrong tool, acting on incomplete information, and reporting success when a step failed. Step limits, input validation, logging every tool call, and requiring confirmation before irreversible actions reduce these risks.
When agents make sense
Agents fit tasks where the path genuinely depends on what is found along the way, such as research, troubleshooting or multi-system requests. If the steps are known in advance, a fixed workflow is usually cheaper and more reliable.
Key takeaways
- An agent lets the model choose its own steps and tools.
- Tool design has a large effect on reliability.
- Keep memory and orchestration as simple as the task allows.
- Guard irreversible actions with limits, validation and human confirmation.
If you are planning an agent that works with your own tools and data, our AI Agent Development team can help design and build it.
AI Agent Development Services- Building effective agentsAnthropic · 2024 · Engineering articleanthropic.com
- ReAct: Synergizing Reasoning and Acting in Language ModelsYao et al. · 2022 · Research paperarxiv.org
- Model Context ProtocolMCP project · Documentationmodelcontextprotocol.io