AI Engineering: From Agents and Harnesses to Production

🤖 Your AI demo can run. But is it ready for production?

You may already have an AI Agent that can call models, use tools and complete tasks.
But once it reaches real users, real data and real permissions, new questions begin:

Are the outputs stable enough?
Will your team know when a tool call fails?
Can you explain and evaluate the quality of its decisions?
Can you control latency and cost?
When something goes wrong, should the system pause, roll back or hand the decision to a person?

A successful demo only proves that something can work once.
A production-ready AI system must also be reliable, testable, observable and clear about its responsibilities.

> When an AI Agent enters a real system, how do we make sure it works reliably — and knows when to stop, ask for help or hand over to a human?

Topic：

1.Agenova - elegant agent governance

2.Agent Loop, Context management, Agent Harness, Evaluations,MCP

3.The Agent Harness: Redefining Security for Autonomous Infrastructure

🛠️ What is missing between Demo and Production?

Many Agents perform well on a developer’s laptop. The real challenge is making them work consistently under real-world constraints:

Model outputs can change
Tools can fail
Permissions may be limited
Data may be incomplete
User requests may go beyond the system’s boundaries
Teams may struggle to reproduce or understand failures

The question is not only whether the model is smart enough. It is whether the whole system is controllable, measurable and ready to improve.
We will look at an Agent workflow through four key layers:

Agent: How does it plan tasks, use tools, handle failure and ask for human help when needed?
Harness: How do tools, orchestration, evaluations, guardrails and human handoffs work together as a testable operating framework?
Production: How do we decide whether an AI feature is ready to launch and continue tracking its quality after release?
Trade-offs: How should teams balance reliability, observability, latency, cost, security, user experience and responsibility?

🔍 This is more than a talk

We will work through real workflows and practical exercises:

Demo-to-Production Ladder
Map the journey from Idea and Prototype to Evaluation, Deployment and Monitoring. Identify what your system is still missing.
Agent Failure Wall
Explore where Agents are most likely to fail: data, tools, permissions, model outputs or human handoff.
Harness Design Clinic
Take a real workflow and break down how tools, orchestration, evaluations, guardrails and human handoffs should fit together.
Production Readiness Roundtable
Discuss the real engineering trade-offs around quality, latency, cost, permissions, security, observability and responsibility.
30-Day Ship Card
Leave with one practical engineering action or product assumption to test in the next 30 days.

You do not need to bring a complete system.
A failed tool call, an unreliable Agent workflow or an AI feature you are unsure about launching can all be useful starting points for discussion.
Bring a real problem. Register for free.

👥 Who should attend?

Engineers working on Agent orchestration, RAG, evaluations, deployment or reliability
Data Engineers, Platform Engineers, DevOps professionals, architects and infrastructure leads
Product managers and engineering leaders deciding whether AI features are ready for real business use
Founders and technical builders moving AI products from idea to production
People who have tried RAG, tool calling or Agents and want to strengthen their engineering foundations
Developers and serious learners moving beyond prompt experiments into AI Engineering

📍 Event Details

Date: Wednesday, 7 October 2026
Time: 5:30 PM–7:30 PM Melbourne time
Location: Amazon MEL12
Address: Level 12.304, 555 Collins St, Melbourne VIC 3000
Language: English
Cost: Free