AI Agents & Autonomous Systems

From ReAct (Reason + Act) to Plan-and-Execute, different agent architectures suit different tasks. We cover single-agent loops, multi-agent hierarchies, and swarm architectures — with guidance on when each pattern is appropriate.

Agents become powerful when they can call APIs, query databases, and interact with external systems. We cover tool definition, parameter validation, error handling, and the security implications of giving AI systems access to real-world actions.

Autonomous systems need safety layers. We implement input validation, action boundaries (what the agent can and cannot do), output verification, cost limits, and human-in-the-loop escalation — creating defense-in-depth for agent deployments.

Complex tasks often require multiple specialized agents working together. We cover supervisor patterns, hierarchical delegation, peer-to-peer collaboration, and the communication protocols that keep multi-agent systems coordinated.

When an agent makes a wrong decision, you need to understand why. We implement trace logging, decision tree visualization, step-by-step replay, and anomaly detection to make agent behavior transparent and debuggable.

Not every decision should be autonomous. We design approval workflows, confidence thresholds, escalation rules, and override mechanisms that keep humans in control of high-stakes decisions while automating routine work.