Agent Architecture

Agentic AI Systems

Reference architecture for building production autonomous agent systems with safety controls.

Models LayerAgents LayerObservability Layer

Agentic AI represents the frontier of enterprise AI deployment — systems that can plan, reason, use tools and execute multi-step tasks autonomously. This blueprint covers production agent design: agent types, tool registries, memory architectures, multi-agent coordination, human-in-the-loop patterns and safety controls.

When to Use This Blueprint

Automating complex multi-step enterprise workflows end-to-end
Building internal AI assistants with CRM, ERP or database tool access
Research and analysis automation requiring iterative reasoning
Customer-facing AI with complex task completion requirements
Process automation where decision sequences are variable and context-dependent

Architecture Components

Agent Type Selection

ReAct agents, Plan-and-Execute, LLM-as-Router. When to use each pattern. Tool-calling vs code-execution agents.

Tool Registry Design

Tool schemas, function calling standards, tool versioning, permission scoping, tool execution sandboxing.

Memory Architecture

In-context memory, external memory (vector), episodic memory, procedural memory. Memory management across long-running agents.

Planning Layer

Task decomposition, sub-goal generation, plan validation. Dynamic replanning on failure. Hierarchical planning for complex tasks.

Human-in-the-Loop Controls

Approval gates, confidence thresholds, action category-based escalation. Interrupt and override mechanisms for production safety.

Execution Environment

Code sandboxing, API rate limiting, tool execution timeouts, retry logic, action logging, reversibility controls.

Agent Observability

Full action trace logging, reasoning chain capture, tool call monitoring, cost tracking, anomaly detection.

Decision Framework

Decision: Agent Pattern

AReAct (reasoning + acting, general purpose)

BPlan-and-Execute (complex multi-step tasks)

CReflexion (self-correcting)

DMulti-Agent (parallelisable tasks)

Decision: Autonomy Level

AFully automated (low-risk, reversible actions)

BHuman-on-the-loop (monitor and override)

CHuman-in-the-loop (approve high-risk actions)

DHuman-controlled (all actions require approval)

Implementation Phases

Week 1–2

Use Case Definition

Task decomposition, tool inventory, autonomy level decision, failure mode analysis, HITL design.

Week 3–5

Agent & Tool Build

Tool registry, agent framework setup, memory system, planning layer, sandbox environment.

Week 6–8

Safety & Controls

HITL integration, approval flows, cost controls, anomaly detection, audit logging.

Week 9–10

Evaluation & Production

Automated evaluation, shadow deployment, performance benchmarking, production rollout.

Governance Controls

Full agent action audit trail with reasoning chain

Tool permission scoping — agents can only access explicitly granted tools

Mandatory approval gates for irreversible actions (email send, data write, payment)

Agent cost budgets with automated throttling

Anomaly detection for unexpected tool usage patterns

Kill switch and graceful shutdown for all production agents

Key Metrics to Track

Task completion rateTool call success rateHuman escalation rateCost per taskAverage task latency

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