Developer Agentic Platform

Most companies we talk to already have a team using a coding agent. Moving to the whole engineering organisation is where it gets hard, and the blockers are rarely the model. We usually find five of them:

• -Nobody can tell what agents cost per team or per change.
• -Each developer connects their own MCP servers with personal tokens.
• -There is no central log of what an agent called or modified.
• -Agents run on laptops, so they stop when the laptop closes.
• -Teams use agents inside the same sprints and handoffs as before, so the gain stays at the level of a faster autocomplete.

• We help you pick the coding agent with data, not demos. Our internal benchmark runs the candidate agents on the same tasks and compares quality, cost and time per task.
• Governance happens at the gateway. Model access, tool permissions and identity are enforced centrally, where your security team can audit them.
• We measure from day one. Cost per token is not enough for a budget review. We track cost per merged change, per team and per model, with the cache hit rate.
• The tool is half of it. The other half is how the team works with it: who writes the intent, who validates the plan, who reviews the code. We update the development lifecycle with the method that fits your teams, AI-DLC being one option among others.
• We go one layer at a time, usually starting with cost, and add the next one once the first is in use. The layer underneath also keeps the agent itself a configuration choice if you switch later.

1. 01

   Governed model access

   Amazon Bedrock behind your identity provider. Inference profiles keep inference in the regions you choose. A proxy such as LiteLLM is optional: it adds per-team keys and budgets, not every company needs it.

2. 02

   Governed tools

   For those who don't know it, MCP is the protocol agents use to call tools. We put an approved catalogue in AWS Agent Registry and expose it through a single Bedrock AgentCore Gateway endpoint, with deny-by-default policies per tool and per group. Web search goes through the same gateway, so developer environments don't need internet access.

3. 03

   Cloud execution

   Agents run headless in your AWS account on AgentCore Runtime. A label on an issue starts the run and the result comes back as a pull request, reviewed by a human.

4. 04

   Prototype hosting

   Agents produce working prototypes. We give them a private URL behind your SSO, an expiry date, and a human approval step before anyone else sees them.

5. 05

   Cost & adoption

   Telemetry from the agents and the gateway lands in one dashboard: usage, cost and output per team.

6. 06

   Way of working

   The development lifecycle itself has to change when an agent writes most of the code: who writes the intent, who validates the plan, how review and testing keep up. Several methods exist for this. AI-DLC (AI-Driven Development Lifecycle), created at AWS, is one of them: the agent proposes, the team decides, and plans and design documents live in the repository. Spec-driven development, with Kiro specs or GitHub Spec Kit, is another. We pick the one that fits your teams and your constraints, then run the first cycles with them on a real backlog item.

• -A 2-hour discovery to see where you stand on the platform layers and on the way your teams work with agents.
• -A 2-week proof of concept on one layer, in your account, or a first cycle of the new way of working with one team on a real backlog item.
• -A first team in production, with the numbers to pick the next layer.