Agent Workflows with Antigravity Training in Kuala Lumpur, Malaysia

Agent Workflows with Antigravity Training introduces participants to designing, managing, and optimizing AI agent workflows using the Antigravity approach—a metaphor for low-friction, self-organizing, and scalable agent systems.

This training focuses on agent orchestration, task delegation, coordination patterns, monitoring, and reliability, enabling teams to build intelligent, autonomous workflows for business operations, automation, analytics, and AI-powered applications.

Introduction to Agent Workflows

• What are AI agents?

• Agent workflows vs traditional automation

• Single-agent vs multi-agent systems

• Why workflows fail and how Antigravity help

Antigravity Principles for Agent Systems

• Removing friction from agent execution

• Autonomy vs control

• Stateless vs stateful agents

• Designing for adaptability and resilience

Agent Architecture Fundamentals

• Agent components and responsibilities

• Memory, tools, and action layers

• Goal-driven and task-driven agents

• Human-in-the-loop designs

Workflow Design Patterns

• Sequential, parallel, and conditional workflows

• Event-driven agent pipelines

• Feedback loops and self-correction

• Agent collaboration pattern

Multi-Agent Coordination

• Role-based agent systems

• Task handoff and negotiation

• Conflict resolution strategies

• Coordination at scale

Tool & API Integration

• Connecting agents to tools and services

• Data sources, APIs, and plugins

• Secure credential handling

• External system orchestration

State, Memory & Context Management

• Short-term vs long-term memory

• Context persistence strategies

• Knowledge sharing between agents

• Avoiding context overload

Reliability & Error Handling

• Failure modes in agent workflows

• Retry, fallback, and escalation patterns

• Monitoring agent decisions

• Recovery and rollback strategies

Observability & Optimization

• Logging agent actions

• Performance and cost tracking

• Workflow bottleneck analysis

• Continuous optimization loops

Security, Governance & Ethics

• Controlling agent permissions

• Audit trails and compliance

• Preventing unintended actions

• Ethical use of autonomous agents

Hands-on Exercises

Summary and Conclusion