Direct answer
Production AI agent development is the engineering work of turning a workflow into an agent an organization can run: defining the goal and tool contracts, bounding permissions and data, evaluating behaviour against fixed cases, instrumenting cost and latency, and gating consequential actions behind a recorded human decision.
AI agent development
Designing, building, and operating an agent that completes real work in production: architecture and orchestration, the tools it may call, the data it may see, the evaluations that prove it still works, and the approval path for anything consequential.
What we build
Agents that do a specific job inside your estate, not general assistants.
Workflow agents
A defined business process end to end - intake, enrichment, decision support, resolution - across the systems it already touches.
Tool and MCP layers
Typed tool contracts over your APIs, databases, and SaaS systems, with authentication and permission boundaries enforced at the contract.
Evaluation harnesses
Fixed cases, graded outputs, and regression gates wired into CI, so behaviour changes are visible before release.
Approval interfaces
The queue where a human sees a proposal with its evidence and approves, edits, or rejects with a reason.
How the work is sequenced
The order matters: we establish what "working" means before building the thing that has to work.
Define the job
The workflow, its systems, its failure modes, and the observable definition of a correct outcome.
Build the evaluation first
Fixed cases drawn from real history, so quality is measurable from the first commit rather than argued about later.
Build the agent and its tools
Architecture, orchestration, typed tool contracts, bounded permissions.
Instrument and gate
Tracing, cost and latency budgets, risk tiers, approval path, audit retention.
Operate and iterate
Decisions and realised outcomes feed back as context, so the system improves on evidence rather than opinion.
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.