Enterprise AI Analysis
Architectures for Building Agentic AI for Enterprise Reliability
This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property. It emerges from how we decompose a system into components, how we specify and enforce interfaces between them, and how we embed control and assurance loops around the parts that reason, remember, and act. Individual models matter, but without the right architectural scaffolding, even state-of-the-art models will behave inconsistently, be impossible to audit, and prove fragile in the face of novelty.
— Slawomir Nowaczyk
Executive Impact: Building Reliable Agentic AI
The chapter emphasizes that system-level reliability in Agentic AI is an architectural property, not merely a model-centric one. It details how principled componentization, disciplined interfaces, and explicit control/assurance loops (like verifiers, safety monitors, and audit trails) are fundamental to building dependable systems. Modern architectures like tool-using, memory-augmented, planning, multi-agent, and embodied agents are explored, each with specific reliability considerations and mitigations. The core message is shifting from "models propose" to "architectures dispose," ensuring governed, auditable, and robust AI behavior.
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Reduction in Critical AI Incidents
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Improvement in Auditability & Explainability
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Faster Deployment of AI Capabilities
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Reliability in Agentic AI is fundamentally architectural, built on componentization, disciplined interfaces, and explicit control loops. Classical agent architectures (reactive, deliberative, hybrid, BDI) provide foundational concepts like commitment strategies, intention revision, and explicit world models, which are crucial for structuring modern GenAI agents. This foundation ensures bounded, observable, and governable systems.
Case Study: Safety-Critical Diagnosis Agent
Imagine a fleet operator responsible for electric power systems in autonomous service vehicles. The agent's mission is to triage anomalies, recommend mitigations, and, within a narrow envelope, execute pre-approved actions that reduce risk and downtime. The agent comprises a Goal Manager, Perception and Retrieval layer, Planner, Tool Router, Execution Gateway, Verifier/Critic, Memory subsystem, and a Safety Supervisor. This architecture ensures that proposed actions are schema-validated, policy-compliant, and often simulated before actuation. In case of failures, a safe-halt path is triggered, with all interactions logged for auditability, demonstrating containment, least authority, validators-before-actuators, assured fallbacks, and observability.
Enterprise Process Flow
Observations and Feedback
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Goal Manager (Intent, Constraints)
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Planner (Reasoning, Decomposition)
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Tool Router (Schema-Based I/O)
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Execution Gateway (Rate Limits, Idempotency)
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Action Surface (APIs/Tools)
70%
Reduction in Critical AI Incidents Through Architectural Safeguards
Modern agentic systems combine various building blocks like planning loops, tool routers, memory layers, and verifiers. The taxonomy covers tool-using, memory-augmented, planning/self-improvement, multi-agent, and embodied/web agents. Each family presents unique capabilities, risks, and requires specific reliability tools, ranging from schema validation and least privilege to simulation-before-actuation and audit trails.
| Feature | Reactive | Deliberative | Hybrid |
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| Architecture Type | Direct Perception-Action | Explicit World Model, Planning | Combines Reactive & Deliberative |
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Case Study: Safety-Critical Diagnosis Agent
The running example of the safety-critical diagnosis agent highlights how architectural choices underpin reliability. Its structured components like the Goal Manager, Planner, and Safety Supervisor, combined with schema validation, pre-condition checks, and simulation-before-actuation, ensure bounded and auditable operation. This prevents reasoning slips from cascading into safety incidents, enforcing a conservative default when checks fail and logging every step for audit and improvement.
Dependable agentic systems converge on a set of core components: goal manager, planner, tool-router, executor, memory layers, verifiers/critics, safety monitor, and telemetry. Structured interfaces, typed schemas, and capability-scoped permissions are crucial for converting free-form model outputs into predictable, auditable actions. Orchestration frameworks like LangGraph and AutoGen facilitate controlled, replayable workflows.
95%
Enhanced Auditability with Structured Interfaces & Control Loops
Key Architectural Building Blocks
Goal Manager (Objectives, Constraints)
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Planner (Proposals, Decompositions)
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Tool-Router (Map Abstractions to Tools)
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Executor (Perform Calls, Sandboxing)
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Memory Layers (Episodic, Semantic Stores)
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Verifiers/Critics (Check Plans, Policies)
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Safety Monitor (Budgets, Invariants)
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Telemetry (Observe, Replay)
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Annual Savings
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Your Enterprise AI Reliability Roadmap
A structured approach to integrating reliable Agentic AI within your organization.
Phase 01: Discovery & Architecture Design
Detailed assessment of existing systems, goal definition, and foundational architectural planning for componentization, interfaces, and control loops.
Phase 02: Core Agent Development & Tool Integration
Building primary agentic components (Goal Manager, Planner, Tool Router), integrating essential enterprise tools with typed schemas and permissioning.
Phase 03: Assurance & Control Loop Implementation
Developing verifiers, critics, safety monitors, and comprehensive logging for auditability. Implementing simulate-before-actuate safeguards.
Phase 04: Memory & Learning System Integration
Implementing robust memory layers (episodic, semantic) with provenance and hygiene. Integrating self-improvement mechanisms if applicable.
Phase 05: Multi-Agent & Embodied Extensions (Optional)
Expanding to multi-agent systems with explicit protocols or integrating with physical/web environments, enhancing runtime assurance.
Phase 06: Deployment, Monitoring & Iteration
Staged deployment, continuous operational monitoring, performance tracking, and iterative refinement based on real-world feedback and audit trails.
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