Catalog
concept#AI#Architecture#Platform#Security

AI Agent

Autonomous software actors that perceive, plan and act to accomplish tasks; an architectural pattern for assistants, automation and distributed systems.

AI agents are autonomous software entities that perceive, plan and act continuously to accomplish tasks.
Emerging
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Message buses and event streaming (e.g., Kafka)Identity and access management systemsData stores, knowledge graphs and vector DBs

Principles & goals

Clear separation of perception, decision logic and actuation.Design for observable states and extensible interfaces.Explicit governance for safety, privacy and accountability.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Misbehavior due to ill-specified goals or faulty reward models.
  • Improper permissioning leads to security and privacy breaches.
  • Excessive automation can reduce human oversight and obscure errors.
  • Explicit interfaces and versioning for agent APIs.
  • Implement fine-grained permissions and audit trails.
  • Simulate and test in isolated environments before production.

I/O & resources

  • Sensory or data inputs (APIs, events)
  • Domain models, roles and permissions
  • Goal definitions, policies and reward profiles
  • Actions against systems, notifications, tickets
  • Logs, metrics and decision histories
  • Analyses, recommendations and training data

Description

AI agents are autonomous software entities that perceive, plan and act continuously to accomplish tasks. Used as an architectural pattern for assistants, automation and distributed multi-agent systems, they define interaction models and lifecycle concerns. This concept outlines design choices such as modularization, state management, security and integration with existing platforms.

✔

  • Enable autonomous automation of complex tasks.
  • Improve responsiveness and scalability via decentralized actions.
  • Support modular, reusable architectures.

✖

  • Require extensive data and context integration for reliable behavior.
  • Challenges in complex coordination and conflict resolution between agents.
  • Potential opacity in decision-making without proper explainability.

  • Task success rate

    Share of tasks successfully completed by agents.

  • Time-to-first-action latency

    Time between event and first agent-initiated action.

  • Mean time to recover (MTTR)

    Average time to detect and remediate misbehavior.

Intelligent chat assistants

Providing context-aware responses and actions in customer support and internal tools.

Auto-scheduling agent

Agent optimizes meetings, resources and invitations based on preferences and availability.

Multi-agent trading bots

Distributed agents simulate trading strategies and coordinate actions in financial simulations.

1

Define agents' goals and responsibilities.

2

Provision data access, APIs and integration points.

3

Design agent architecture (Perception, Decision, Actuation).

4

Implement monitoring, governance and security controls.

⚠️ Technical debt & bottlenecks

  • Ad-hoc state storage without a migration strategy.
  • Tight coupling to proprietary APIs instead of clear abstractions.
  • Insufficient testing and simulation infrastructure.
Data access latencyCoordination overheadState replication
  • Agent makes financial decisions without human oversight.
  • Agent granted overly broad permissions and exfiltrates data.
  • All business logic implemented inside a monolithic agent.
  • Unclear goal definitions lead to unexpected behavior.
  • Ignoring security boundaries in test environments.
  • Missing observability makes root causes hard to find.
Architecture and system design for distributed systems.Knowledge of AI models, state management and observability.Security, privacy and compliance expertise.
Required autonomy and reaction timeData access and context integrationSecurity and governance requirements
  • • Restricted data access due to privacy regulations.
  • • Limited compute capacity in edge environments.
  • • Regulatory requirements for traceability.