Direct answer
A dedicated AI engineering team is an embedded pod that builds agentic systems inside your organization, working in your repositories and your review process. It suits companies that intend to own the capability long term: the pod delivers working systems while transferring the practice to your own engineers.
Dedicated AI engineering team
An engagement model rather than a product. A small group of agent, integration, and product engineers works as part of your team on your systems, under your engineering standards, with capability transfer treated as a deliverable.
How a pod is composed
Shaped to the work rather than sold as a fixed bundle.
Agent engineers
Agent architecture, orchestration, tool use, evaluation design.
Integration engineers
MCP and tool contracts, APIs, data access, authentication and permissions.
Product engineers
The application, the approval interfaces, and the operational surface people use.
Technical review
An architecture and quality gate outside the delivery pod, so decisions get challenged before they set.
What capability transfer means in practice
A deliverable with evidence, not a promise in a proposal.
Your repositories
Work lands in your codebase under your review process from the first week, not handed over at the end.
Documented decisions
Architecture decisions are recorded with their reasoning, so your engineers inherit the why alongside the code.
Paired delivery
Your engineers work on the same tickets, with the pod reviewing rather than owning as the engagement progresses.
Runbooks and evals
Operational runbooks and the evaluation suite are part of the deliverable - the parts you need to run it without us.
The Propose–Decide–Execute pattern
CodeDTX builds agentic systems on the Propose–Decide–Execute pattern: agents may only write proposals with evidence attached, a named human records an approval or rejection with a reason, and a separate execution layer carries out approved work and logs the artifact. No agent holds a write tool to the outside world.
- 01
Propose
The agent analyses live system state and drafts a change, with the evidence it relied on attached to the proposal.
- 02
Decide
A named human approves, edits, or rejects with a reason. Risk tier determines who is allowed to decide.
- 03
Execute
A separate execution layer performs approved work - merge, publish, call, write - and records the resulting artifact.
- 04
Audit
Actor, reason, evidence, artifact, tokens, and cost are retained for every run, so any decision can be reconstructed later.
The six layers we build and review against
- 1
Agent layer
Agent architecture, tool use, memory and context, multi-agent patterns, structured outputs, orchestration.
- 2
Integration layer
MCP servers, tool contracts, API and database adapters, authentication, permissions, legacy system access.
- 3
Knowledge layer
Retrieval and RAG, vector and search architecture, enterprise knowledge sources, data access controls.
- 4
Reliability layer
Evals, tracing, observability, cost and latency budgets, fallbacks, regression tests.
- 5
Safety layer
Guardrails, prompt-injection defense, PII and data boundaries, human-in-the-loop gates, audit trails.
- 6
Product layer
The application people actually use: interfaces, approval queues, and operational runbooks.
Frequently asked
Have a workflow that should become AI-enabled?
Tell us about the system it lives in. We reply from an engineering seat, not a sales deck.