Agent Reliability Engineering

Building the agent is the easy part

The work starts in production. Prompts change, tools change, users behave in ways nobody tested, and an agent that was accurate last month starts drifting. Traditional software is deterministic and agents are not, so the usual monitoring misses most of what goes wrong.

Agent reliability engineering applies SRE practice to that problem. We cover four areas.

1. Semantic monitoring and hallucination detection

Standard monitoring catches a 500 error. It does not catch a confident wrong answer. We check outputs against ground truth in your data, such as BigQuery, and watch for drift in tone and policy.

• Ground truth checks on live responses
• Automated hallucination scoring
• Persona and policy adherence checks

2. Prompt CI/CD and rollback

One word changed in a system prompt can break a multi-step tool call. We version and test prompts the way you would any other code.

• Regression tests against golden datasets
• Automatic rollback when quality metrics drop
• Canary releases on live traffic

3. Token cost and latency

Unbounded reasoning loops run up the bill and slow responses down.

• Model routing: a fast model for triage, a larger one for reasoning
• Circuit breakers on tool call loops
• Context pruning and prompt compression

4. Tool guardrails and security

An agent with API access needs tight boundaries so a prompt injection cannot reach further than intended.

• Authenticated Model Context Protocol (MCP) servers
• Least privilege IAM on Cloud Run
• Audit trails that support APRA CPS 234

How we deliver it

We build the reliability platform on Cloud Run, Vertex AI and Cloud Monitoring, run it with your team, and hand it over or keep running it as a managed service.