Context and work plan
Reading the repository, business rules and dependencies to define tasks and acceptance criteria.
Your software evolving with AI agents and engineering direction.
Combine AI and engineering agents to analyze, implement, test, and evolve software with human review and acceptance criteria.
What we deliver
A scope built from your process, with deliverables that the team can validate and use.
Agent-assisted deployment
Agents support code changes, integrations and documentation in delimited tasks, under engineering coordination.
Validation and review
Relevant testing, change analysis, and human review before integrating or publishing a deliverable.
Delivery and continuity
Versioned changes, recording of decisions and transferring the context to the client's team.
How does this appear in operation
Illustrative example of application; does not represent a measured customer outcome.
The challenge
An integration waits in the backlog: it requires understanding the legacy, changing contracts and validating failure scenarios.
The proposed solution
Engineering divides work into tasks; Agents support analysis, code, and testing. Changes undergo review, approval and approval before publication.
Start with clarity. Evolve with evidence.
We start with a diagnosis and a pilot cycle on a real section of the backlog. Continuity combines engineering, agents, and review into prioritized cycles compared to the project baseline.
Agentic AI does not eliminate the need for engineering, review, or testing. Gains vary depending on the system and are measured in the pilot. Access to environments, use of providers and approval of publications are defined with the client.
What do we need to align
- Authorized access to the repository and definition of code and data that can be used with AI.
- Customer responsibility for priorities and acceptance, as well as suitable environments for validation.
How we evaluate work
- Cycle time by task type
- Rework and failures after delivery
- Acceptance criteria met and cost per cycle
Before you start
What is agentic AI in software development?
It is the use of agents capable of performing steps of a task with tools and context: analyzing files, proposing changes, implementing and executing checks. In our delivery, this work is delimited and supervised by engineering.
What's the difference to just using an AI chat?
The work connects to the repository, project rules, and delivery checks. The result is a reviewable software change, with acceptance criteria, and not just a code suggestion.
Do you guarantee faster or cheaper development?
We do not promise a universal percentage. We measure cycle time, cost, rework and quality in the pilot to decide where the use of agents generates value.
Can they work on our current system?
Yes, subject to analysis of architecture, access and test conditions. Action can begin with maintenance, integration or a limited functionality, without requiring rewriting of the system.
Who approves the code and protects the project context?
Engineering reviews the changes and the customer participates in acceptance. Permissions, permitted data and tools are agreed before use; publication follows the project approval flow.
Let's look at your scenario?
Tell us which process needs to be improved, the systems involved and the company’s current situation. We will analyze the context and respond via email.
Service of interest: Development with agentic AI. After the initial analysis, we decide whether the next step is a diagnosis, a pilot or a proposal.
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