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From chatbots to agents
A chatbot takes a prompt and returns text. Real operational work is different: it spans several systems, keeps state and fails in ways that need judgement.
When a chatbot hits an API timeout, it does not know whether to retry, escalate or try another route. It loops, burns tokens or stops, and a person has to step in. That is still manual work, with AI in the middle.
Example: vendor onboarding
Vendor onboarding is often a slow, manual process across several portals. An agent takes the goal, runs identity checks, updates the CRM, sets up billing in the ERP and routes the contract for approval.
What changes for your team
The value is in what your people get back when agents take on the routine operational work.
Engineering
Less glue code, more product work
Engineers spend too much time writing integrations and looking after fragile scheduled jobs. Agents take over that integration layer so your engineers can work on your product.
Data and analytics
Less extraction, more analysis
Analysts stop pulling CSVs, cleaning data and formatting slides by hand. Agents query BigQuery in plain language, check data quality, spot anomalies and draft summaries for review.
Operations
Fewer status chases
Leaders stop chasing updates across disconnected systems. Agents monitor workflows around the clock, clear routine blockers and send notifications with a full audit log.
What our agents do
Stateful task execution and coordination across your systems.
Goal planning
Give the agent an objective, such as "Onboard this vendor and check compliance against APRA CPS 234", and it works out the prerequisites, sequences the steps, queries the data it needs and carries the task through.
Connected to your systems
Agents connect to your CRM (HubSpot, Salesforce), ERP, BigQuery and internal REST or gRPC APIs through the Model Context Protocol (MCP) and scoped Google Cloud service accounts.
Error handling
When an API times out or a schema changes, a basic bot stops. Our agents read the error, adjust, try a fallback, and notify a person only when they need to.
Multi agent teams
Specialised agents, such as a compliance checker, a data analyst and a technical writer, work together and check each other's output, orchestrated with the Google Agent Development Kit (ADK).
Measured on business results
Every agent system is measured against cost, risk and engineering effort.
For CEOs and boards
Lower cost per transaction and faster delivery
- Fewer hand offs between business units.
- Less human error in compliance, finance and vendor onboarding.
- Handle more volume without adding headcount at the same rate.
For CTOs and engineering leads
Production architecture on Google Cloud
- Built on Vertex AI, the Agent Development Kit (ADK) and Cloud Run.
- Evaluation pipelines and LLM as a judge checks before release.
- Infrastructure defined in Terraform, so environments stay consistent.
For CISOs and compliance teams
Governance and data boundaries
- Human approval gates for critical decisions.
- VPC Service Controls to keep data inside defined perimeters.
- Audit trails for every API call, reasoning step and state change.
Why Aviato
Autonomous AI without guardrails is a liability. We build in human approval, data isolation and ongoing reliability engineering.
Human approval where it matters
You set the limits. Decide which actions run automatically and which pause for approval in Slack, Teams or email.
Your data stays yours
Data never leaves your private Google Cloud VPC. No customer IP is ever shared with external models or used for foundation training.
24/7 reliability engineering
Once your agents are live, our SRE team monitors latency, prompt drift, API rate limits and schema changes around the clock.
Talk to an architect who has done this before.
Bring your current setup and the outcome you need. You will get a view on the approach, the risks and roughly what it costs.
Straight to a senior GCP architect. No SDR, no slide deck.
Not ready to talk? See how we migrated Hapana off AWS →
Or call +61 2 8359 9507 · Hello@aviato.consulting