Building Autonomous AI Agents with Tool Calling & Memory

# Building Autonomous AI Agents with Tool Calling & Memory

Autonomous agents are transitioning from novelty conversational bots into deterministic software workers. To build systems that companies can trust, developers must move beyond naive prompt concatenation.

The Core Loop: Perception → Decision → Execution → Verification

A production agent loop requires:

1. **Structured Tool Schemas:** Declaring JSON Schema parameters with strict types. 2. **Deterministic Dispatcher:** Routing LLM tool call requests to validated local functions. 3. **Resilient Error Recovery:** Feeding tool errors back to the model rather than crashing. 4. **Episodic Memory Logs:** Recording actions and observations for auditability.

const agent = createAgent({
  tools: [searchKnowledgeBase, executeCodeSandbox, inspectDatabaseSchema],
  maxIterations: 6,
  onToolError: (error) => `Execution failed: ${error.message}. Please adjust parameters.`
});

Memory Architectures Compared

While vector databases provide broad associative search, structured SQLite or PostgreSQL logs provide deterministic chronological replayability. The best production systems combine both: vector search for historical domain knowledge, and structured relational tables for current task state.

Join us in the **Autonomous AI Agents & Memory Pipelines Workshop** to build a functional code review agent from scratch!