Catalog
concept#AI#Software Engineering#Integration#Product

Agent-based Assistance

Concept of autonomous software agents that provide contextual support to users and processes.

Agent-based assistance denotes use of autonomous software agents to support users and processes.
Emerging
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Ticketing and CRM systemsAuthentication and authorization servicesMonitoring and observability platforms

Principles & goals

Transparency in agent decisions and actionsPrivacy by design and minimal data retentionIterative monitoring and continuous improvement
Build
Domain, Team

Use cases & scenarios

Compromises

  • Misbehavior due to incorrect training data
  • Violation of privacy and compliance requirements
  • Overautomation leading to loss of accountability
  • Start small: focused domains and clear KPIs
  • Integrate privacy-by-design in architecture and data flows
  • Ensure regular reviews and human oversight

I/O & resources

  • Access to relevant data sources (logs, CRM, calendar)
  • Definition of business rules and escalation flows
  • Security and compliance requirements
  • Automated actions and suggestions
  • Auditable decision logs
  • Operationalized metrics for evaluation

Description

Agent-based assistance denotes use of autonomous software agents to support users and processes. It combines user modeling, task orchestration and adaptive learning, often using AI/ML, to deliver contextual recommendations and automation. Implementation requires integration, privacy safeguards and operational monitoring, and continuous evaluation.

✔

  • Automation of repetitive tasks reduces effort
  • Contextual recommendations increase user satisfaction
  • Scalable assistance across heterogeneous systems possible

✖

  • High integration effort into existing systems
  • Requires valid data foundation for reliable recommendations
  • Limits in explainable decision-making

  • Automation rate

    Share of tasks fully executed by agents.

  • Recommendation accuracy

    Correctness of suggested actions compared to ground truth.

  • Time-to-resolution

    Average time until a process step or ticket is resolved.

Intelligent scheduling assistant

Agent coordinates calendars, proposes times and autonomously handles delegations.

Automated support agent in customer service

Agent prioritizes tickets, suggests resolutions and escalates when needed.

Product decision assistant

Agent analyzes usage data and provides prioritized feature recommendations.

1

Prioritize use cases and define metrics

2

Build data integration and ensure baseline quality

3

Run pilot with monitoring, feedback loops and governance

⚠️ Technical debt & bottlenecks

  • Ad-hoc integrations without API contracts
  • Unstructured storage of training data
  • Missing test infrastructure for agent scenarios
Data quality and accessLatency of external integrationsModel and rule management
  • Autonomous escalation without human review in critical cases
  • Collecting sensitive user info for personalization without purpose limitation
  • Use in safety-critical systems without redundancy
  • Overestimating model accuracy on production data
  • Insufficient monitoring alerts for misbehavior
  • Neglecting governance during rapid iteration
Knowledge in software architecture and integrationsExperience with ML modeling and evaluationUnderstanding of privacy and compliance
Real-time integration of heterogeneous systemsExplainability and traceability of decisionsPrivacy and compliance
  • • Legal privacy requirements
  • • Limited access rights to systems
  • • Budget and personnel constraints