Data & AI

AI Agent System Requirements & Onboarding Concept

Data & AI

Motivation

The AI Hybrid Workforce is a heterogeneous team composed of both humans and AI agents. It operates in a collaborative environment where AI agents augment human capabilities by autonomously handling simple, repetitive tasks. As a result, human team members can focus more on complex, high-value activities. This division of labour significantly enhances the hybrid team's performance in terms of scalability, efficiency, and effectiveness, when compared to a team composed solely of humans.

This offering delivers a structured path from having a simple business concept for an AI agent towards becoming fully prepared for the technical implementation and agent onboarding phases.

Internal human job roles tend to be generally well defined; however, the specific tasks, decision-making scenarios, and dependencies within these roles are often insufficiently documented. Requirements may lack clarity, and system landscapes can be fragmented. While data sources might be available, concerns regarding their quality, accessibility, and compliance status frequently arise. In addition, employees may operate within highly regulated environments that impose stringent requirements on privacy, security, and compliance.

In preparation for the implementation and operation of an AI agent, a cross-functional team of business, AI, and data experts will analyse the target job role, define technical and operational requirements, evaluate data sources and system readiness, and create a system architecture for the proposed agent.

The output is an implementation plan including developmental and operational cost estimates for the agent. As such, you can calculate the ROI on the agent case and make a management decision to proceed to the implementation and operational phases.

What we bring

Over 20 years of experience with software and data architectures for many of the world’s largest enterprises has generated the expertise needed for PRODYNA to help you design and develop applications in the cloud. With this offering we will assist you with:

With this offering we will assist you with:

  • Expertise in AI agent design, requirements engineering, and enterprise AI strategy
  • Structured methods for task evaluation, data quality assessment, and governance review
  • Experience in bridging business goals with technical feasibility
  • Deliverables that ensure you can start implementation immediately

What you need

To ensure a successful outcome, we rely on your team’s business knowledge and openness to share and challenge existing ways of working. The following elements are essential:

  • Access to business stakeholders with detailed process knowledge
  • Availability of system owners and technical documentation (APIs, interfaces, integration options)
  • Access to relevant data sources and key personnel for data evaluation
  • Willingness to engage in review cycles and workshops
  • Cross-functional participation from Business, IT, HR, and compliance

What you get

By the end of the workshop, you will have:

  • A validated and refined task-based activity map for your AI agent.
  • A requirements specification including acceptance criteria and KPIs.
  • A system integration map detailing the involved IT systems.
  • A data quality report with gap analysis and readiness roadmap.
  • A governance, security, and compliance framework for safe AI adoption.
  • A system architecture with estimates for the implementation and operational costs.
  • An executive summary with decision support and next steps.

PRODYNA will guide you and your employees through the process in the following phases:

Kick-off & Goal Alignment

  • Align objectives, scope, ownership, and success criteria in a joint workshop.
  • Review the target job role and automation goals.
  • Identify legal, security, and regulatory aspects relevant to governance.

Deliverable: Discovery objectives & refined scope.

Read more

Requirements Engineering

  • Define functional and non-functional requirements, plus acceptance criteria
  • Gather inputs through stakeholder interviews and iterative validation
  • Document measurable success metrics for the AI agent.

Deliverable: Requirements specification with acceptance criteria

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System Integration

  • Analyse how the selected role-based tasks interact with existing systems and data flows.
  • Identify technical dependencies, APIs, and integration options required to implement the AI agent.
  • Assess feasibility of automation for top-ranked tasks, considering system readiness and organizational impact.

Deliverable: System integration map detailing the involved IT systems and their interactions with the agent.

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Data Quality & Availability Assessment

  • Evaluate data relevance, completeness, and accessibility across relevant sources.
  • Run gap analysis to identify improvements required for agent implementation.
  • Perform small-scale tests with sample data and pre-built models.

Deliverable: Data quality report, gap analysis & readiness roadmap.

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AI Governance, Security & Compliance Review

  • Assess transparency, traceability, fairness, robustness, and privacy aspects.
  • Document compliance requirements and potential risks.
  • Align with existing governance or CoE strategy.

Deliverable: Governance & compliance requirements document

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System Architecture

  • Sketch candidate architecture options and tool chains
  • Estimate effort, cost, and risks of implementation
  • Highlight quick wins and long-term investment scenarios

Deliverables: System architecture document. Cost estimates, Implementation roadmap

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Summary & Recommendation Workshop

  • Present findings and deliverables to stakeholders
  • Discuss trade-offs, priorities, and pilot options
  • Provide decision support and define concrete next steps

Deliverable: Executive summary & recommendation package

Read more

Quick facts

  • Duration: 2-4 weeks
  • Team: Business Analyst, AI Engineer, Data Engineer
  • Format: Combination of workshops, interviews, and in-depth analysis

Benefits

  • Includes all preparatory steps to move from the concept to the implementation phase.
  • Clarifies all functional and non-functional requirements including business, technical, security and compliance aspects.
  • Clarifies the required system landscape including integration points.
  • Analyses the available data regarding quality, availability and security.
  • Provides a governance framework aligned with organizational and regulatory standards.
  • Produces a solution architecture with cost and risk indicators
  • Provides clear decision support for implementation

Want to hear more?

Contact me

Lukasz Obst

Lead Data Architect
Dusseldorf
Contact me
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