End-to-end agentic AI use cases

Most companies we talk to have more agent ideas than agents in production. The prototype convinces in a demo, then stops before going live, usually for one of these reasons:

• -The use case was picked because it demoed well, not for its value or for the data available.
• -Nobody agreed on what good enough means, so the go-live decision becomes a matter of opinion.
• -The agent works on test data but has no access to the systems it must act on: core banking, CRM, ERP.
• -Legal or compliance raises the risks of a customer-facing agent at the end of the project instead of at the start.

• We start from the outcome, written as if the agent were already live: who uses it, what changes for them, how we measure it. Then we check that the data and the systems exist.
• Success criteria are agreed before the build: the test set, the accuracy threshold, the cost per conversation. Go-live is decided on these numbers.
• An agent that acts needs more care than one that answers. Actions on your systems go through approvals, limits and audit, and a human stays in the loop where the risk is.
• Customer-facing agents are designed with legal and compliance from the first week: tone of voice, telling users they talk to an AI, handoff to a human, data retention.
• The team that builds the agent takes it to production and runs it. No prototype handed over to a team that did not build it.

1. 01

   Framing

   A short working backwards document: the users, the value, the data and systems needed, the risks. It ends with a go or no-go and a costed plan.

2. 02

   Prototype on real data

   In your AWS account, connected to the real systems in read-only mode or to a copy, and in front of a few real users within weeks.

3. 03

   Evaluation

   A test set built from real cases with the expected outcome, scored on every change. Customer-facing agents also get adversarial tests: prompt injection, off-topic requests, attempts to extract personal data.

4. 04

   Integration & guardrails

   Connections to your systems through their APIs or MCP, Bedrock Guardrails on inputs and outputs, approval steps before sensitive actions, and a handoff to a human with the conversation context.

5. 05

   Production & operations

   Quality, cost and latency are monitored. User feedback and failed conversations feed the test set, and a runbook says who does what when the agent misbehaves.

• -A 2-hour discovery on your list of use cases, to pick the one with the best balance of value and risk.
• -A framing week ending with a go or no-go and a costed plan.
• -A prototype in front of real users within 4 to 6 weeks, then production.