Start with the work around the AI
Gathering context. Copying results. Checking outputs. Asking for the next step. We map one recurring job and identify which parts a system can handle and which decisions your team should keep.
What is agentic AI development?
Agentic AI development means building software that uses an AI model to interpret inputs, choose steps and use tools within defined limits. A working system also needs permissions, output checks, failure handling and a way to return decisions to a person.
MCP, or Model Context Protocol, is one way to connect an AI application to tools and data. The choice of connector follows the workflow.
What could your team hand over?
Examples to assess together, not claims about client deployments:
| Job | The system could | Your team keeps |
|---|---|---|
| Software delivery | Plan, build against tests and prepare a delivery report | Plan and release approval |
| Agency reporting | Gather permitted data and draft a report | Interpretation and client delivery approval |
| Support preparation | Find approved documents and draft a cited answer | Exceptions and sensitive responses |
Read the agency workflow example for more detail.
What a scoped build includes
- A workflow specification: inputs, outputs, limits and acceptance criteria.
- The agent and integrations: agreed tools, permissions and saved workflow state where needed.
- Tests and evaluations: software checks plus representative tasks, including failure cases.
- Human approval points: clear gates before agreed high-impact actions.
- Deployment and handover: run instructions, logs, known limitations and maintenance responsibilities.
Passing code tests is only part of the evidence. Evaluations check whether the agent handles real tasks and tool calls appropriately. See how I evaluate agents before shipping.
From one workflow to a working system
- Map. Review a real run, establish a baseline and agree the pilot's success criteria.
- Build. Implement one useful path and review its outputs with your team.
- Roll out. Test failures, confirm access and document how to operate the system.
We expand only after reviewing usage and results. The working method explains the approach.
Inspect a working example
raza-Developer puts a development process around an AI: approved plan, tests, evaluation and a delivery report.
The published walkthrough describes one run with 7 of 10 steps automated. That is a share of steps, not time or cost saved. Your workflow needs its own baseline.
Questions before building
Do we need an agent or ordinary automation?+
Predictable steps may only need ordinary automation. An agent is useful when the job needs to interpret changing inputs or choose tools and next steps. We assess that first.
Can you use our existing tools and documents?+
We check APIs, connector support, data readiness and access requirements before agreeing scope. Any missing access or integration limit becomes part of the plan.
Will it act without approval?+
Only within agreed permissions. We define which actions may run automatically and which must wait for a person, then implement those boundaries.
What happens when it makes a mistake?+
We test missing inputs, failed tool calls and unsuitable outputs. Depending on the job, the system retries within limits, stops or sends the work for review.
What will it cost, and how long will it take?+
That depends on integrations, data, evaluation and rollout needs. We agree fees, milestones and responsibilities before building, including expected model and hosting costs.
Who maintains it after handover?+
We agree an operating owner and any ongoing support. The handover explains how to run it, what was checked and what remains limited.