Building Agentic AI — Multi-Step Agent Workflows

In 2024, the conversation shifted from "Chatbots" to "Agents." An agent doesn't just answer questions; it takes actions. Here is how we build autonomous agents at Parallel Loop.

The Agent Loop Architecture

A true agent operates on a continuous loop: Perceive -> Plan -> Act -> Reflect.

1. Perceive: The agent gathers context from its environment (DBs, APIs, Web Search).
2. Plan: Using an LLM, the agent breaks a complex goal into smaller sub-tasks.
3. Act: The agent uses "Tools" (functions) to execute those sub-tasks.
4. Reflect: The agent checks the output of its action and decides if the goal is met.

Implementing Tool-Calling (Function Calling)

OpenAI and Anthropic now support structured Function Calling. This is the backbone of agentic behavior.

• Define a JSON schema for your tools (e.g., `send_email`, `query_database`).
• The model outputs the tool name and arguments.
• Your backend executes the code and returns the result to the model.

Memory Systems: Short vs. Long Term

Autonomous agents need memory to handle multi-step tasks.

• Short-term Memory: The current conversation window (preserved via token management).
• Long-term Memory: A vector database (like Weaviate) where the agent can store and retrieve past experiences and user preferences.

Guardrails and Safety

Autonomous agents can behave unpredictably. We implement:

• Human-in-the-loop (HITL): Requiring approval for high-stakes actions (like processing a payment).
• Outcome Validation: Using a second, smaller LLM to verify that the agent's output follows safety guidelines.

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Frequently Asked Questions

What is agentic AI vs a chatbot?

Agents plan multi-step tasks, call tools/APIs, and loop until a goal is met. Chatbots respond to single turns. Agents need stronger guardrails and cost caps.