Architecture

Agentic AI Systems: Reliable LLM Agents with Tools, Memory, and Guardrails

Comprehensive guide to Agentic AI Systems: Designing Reliable LLM Agent Architectures with Tool Use, Memory, and Guardrails for software engineers

By Architecture Team February 26, 2026 13 min read
Agentic AI Systems: Reliable LLM Agents with Tools, Memory, and Guardrails

Building intelligent AI agents with tools, memory, and safety guardrails

Agentic AI Systems: Reliable LLM Agents with Tools, Memory, and Guardrails

One-line description: A practical guide to designing production-grade LLM agents with tool use, memory, and safety guardrails in cloud-native distributed systems.
Tags: LLM Agents, System Design, Distributed Systems, Tool Calling, RAG, Observability, Guardrails

Introduction

Agentic AI systems—LLM-driven services that can plan, call tools, and take multi-step actions—are rapidly moving from demos to production workloads: customer support automation, incident triage, data analytics copilots, internal developer assistants, and workflow orchestration across SaaS tools.

This topic matters because the jump from “chatbot” to “agent” multiplies failure modes:

  • Agents act (write to databases, trigger deployments, email customers), so mistakes have real cost.
  • Agents are distributed (tools are remote services), so latency, retries, timeouts, and partial failures become central.
  • Agents need memory (context across sessions), so you must design storage, privacy, and consistency.
  • Agents need guardrails (security, safety, policy), so you must constrain what the model can do and verify outcomes.

In interviews, “agent design” is increasingly a system design prompt variant: Design an AI assistant that can resolve tickets, query internal knowledge, and execute approved actions safely. Interviewers expect you to reason like a distributed systems engineer: contracts, idempotency, observability, and blast-radius control—while incorporating LLM-specific constraints like hallucinations, prompt injection, and evaluation.

This article focuses on reliable LLM agent architectures in cloud-native distributed systems, with practical code patterns, diagrams, and interview-ready talking points.

Core Concepts

1) What is an “Agent” (vs. Chat Completion)?

A basic chat completion returns text. An agent is a loop:

  1. Interpret user intent
  2. Plan steps
  3. Call tools (APIs, DB queries, search)
  4. Observe tool outputs
  5. Decide next step or finalize

Key properties:

  • Tool use: model chooses which tool and with what arguments
  • Statefulness: short-term scratchpad + long-term memory
  • Autonomy: multi-step execution with stopping conditions
  • Verification: outputs validated against policies and schemas

2) Tool Use: Contracts, Not Prompts

In production, treat tools like RPC endpoints with strict contracts:

  • Typed inputs/outputs (JSON schema / OpenAPI)
  • AuthN/AuthZ and scoped credentials
  • Timeouts, retries, circuit breakers
  • Idempotency keys for write actions
  • Audit logs for every call

LLM tool calling should be constrained:

  • The model can request tool calls
  • The orchestrator decides whether to execute
  • The tool results are returned verbatim (or sanitized) to the model

3) Memory: Short-Term vs. Long-Term

Common layers:

  • Conversation state (short-term): last N turns, current plan, tool results.

    • Stored in Redis / in-memory cache for speed
    • Often “windowed” or summarized to fit context limits

  • Episodic memory (long-term): user preferences, past tasks, outcomes.

    • Stored in Postgres / document store
    • Retrieved by user/session keys

  • Semantic memory (RAG): embeddings over docs, tickets, runbooks.

    • Stored in vector DB (pgvector, Pinecone, Milvus, Elasticsearch)
    • Retrieved by similarity + filters + recency

Important: memory is an input, not truth. You must handle staleness, access control, and prompt injection.

4) Planning & Execution Models

Common approaches:

  • ReAct-style loop: think → act(tool) → observe → repeat.
  • Plan-and-execute: create a plan, then execute steps deterministically.
  • State machine / workflow: explicit states (triage → diagnose → resolve).
  • Hierarchical agents: manager agent delegates to specialist sub-agents.

In interviews, emphasize that “free-form autonomy” is risky; production systems often converge to bounded autonomy with workflow constraints.

5) Guardrails: Defense in Depth

Guardrails are layered:

  1. Input guardrails: prompt injection detection, PII detection, policy checks.
  2. Tool guardrails: allowlists, argument validation, scoped permissions.
  3. Output guardrails: schema validation, toxicity checks, redaction, citations.
  4. Runtime guardrails: budgets (time/steps/cost), kill switches, rate limits.
  5. Human-in-the-loop: approvals for high-risk actions.

A key architectural principle: the LLM is not the security boundary.

6) Reliability in Distributed Agent Systems

Agents amplify distributed systems issues:

  • Tool flakiness: transient 5xx, rate limits
  • Long-running tasks: multi-minute workflows require async orchestration
  • Exactly-once is hard: rely on idempotency + dedupe
  • Consistency: memory updates vs. tool side effects
  • Observability: need traceability across model calls and tool calls

Treat the agent orchestrator as a workflow engine with:

  • durable state
  • retries with backoff
  • compensation (saga) for partial failures
  • structured logs + traces

Implementation Details

Reference Architecture (Cloud-Native Agent Platform)

flowchart LR
  U[Client UI / API] --> GW[API Gateway]
  GW --> ORCH[Agent Orchestrator Service]

  ORCH --> LLM[(LLM Provider)]
  ORCH --> MEM[Memory Service]
  ORCH --> RAG[RAG Retrieval Service]
  ORCH --> POL[Policy/Guardrail Service]

  ORCH --> BUS[(Event Bus / Queue)]
  BUS --> WORK[Async Workers]

  WORK --> TOOLS[Tool Services]
  TOOLS --> DB[(Databases)]
  TOOLS --> SAAS[(External SaaS APIs)]

  ORCH --> OBS[Observability\nLogs/Traces/Metrics]
  WORK --> OBS
  MEM --> OBS
  POL --> OBS

Key design choices:

  • Orchestrator is stateless; durable state lives in Memory/DB.
  • Tool execution can be synchronous (fast reads) or async (writes, long tasks).
  • Guardrails are centralized in a policy service for consistency and auditing.

A Minimal Agent Orchestrator (Python)

Below is a practical pattern: the LLM proposes tool calls; the orchestrator validates and executes them.

Tool definitions with strict schemas

from dataclasses import dataclass
from typing import Any, Dict, Optional, Callable
import jsonschema

@dataclass
class Tool:
    name: str
    description: str
    input_schema: Dict[str, Any]
    handler: Callable[[Dict[str, Any], Dict[str, Any]], Dict[str, Any]]  # (args, ctx) -> result
    requires_approval: bool = False

def validate_args(schema: Dict[str, Any], args: Dict[str, Any]) -> None:
    jsonschema.validate(instance=args, schema=schema)

Example tools

import time
import uuid

def search_kb(args: Dict[str, Any], ctx: Dict[str, Any]) -> Dict[str, Any]:
    # Pretend retrieval; in real life call your RAG service
    query = args["query"]
    return {
        "query": query,
        "results": [
            {"doc_id": "runbook-123", "title": "Reset Password Runbook", "snippet": "Steps to reset..."},
            {"doc_id": "policy-7", "title": "Account Security Policy", "snippet": "MFA required..."},
        ],
    }

def reset_password(args: Dict[str, Any], ctx: Dict[str, Any]) -> Dict[str, Any]:
    # Write action: must be idempotent and audited
    user_id = args["user_id"]
    idem = args.get("idempotency_key") or str(uuid.uuid4())

    # Simulate side effect
    time.sleep(0.2)
    return {"status": "ok", "user_id": user_id, "idempotency_key": idem}

TOOLS = {
    "search_kb": Tool(
        name="search_kb",
        description="Search internal knowledge base for procedures and policies.",
        input_schema={
            "type": "object",
            "properties": {"query": {"type": "string", "minLength": 1}},
            "required": ["query"],
            "additionalProperties": False,
        },
        handler=search_kb,
    ),
    "reset_password": Tool(
        name="reset_password",
        description="Reset a user's password (requires approval).",
        input_schema={
            "type": "object",
            "properties": {
                "user_id": {"type": "string", "minLength": 1},
                "idempotency_key": {"type": "string"},
            },
            "required": ["user_id"],
            "additionalProperties": False,
        },
        handler=reset_password,
        requires_approval=True,
    ),
}

Orchestrator loop with guardrails

This example uses a simplified “LLM response” format. In production you’d use native tool-calling APIs, but the control flow is similar.

from typing import List

class PolicyError(Exception): ...
class ToolDenied(Exception): ...

def policy_check_tool_call(tool: Tool, args: Dict[str, Any], ctx: Dict[str, Any]) -> None:
    # Example: enforce allowlist + tenant scoping + approvals
    if tool.name not in ctx["allowed_tools"]:
        raise ToolDenied(f"Tool not allowed: {tool.name}")

    if tool.requires_approval and not ctx.get("approved", False):
        raise PolicyError(f"Approval required for tool: {tool.name}")

def policy_check_user_input(user_text: str, ctx: Dict[str, Any]) -> None:
    # Placeholder: add prompt injection / PII checks here
    if "ignore previous instructions" in user_text.lower():
        raise PolicyError("Prompt injection attempt detected")

def run_agent(user_text: str, llm, ctx: Dict[str, Any], max_steps: int = 6) -> str:
    policy_check_user_input(user_text, ctx)

    messages: List[Dict[str, str]] = [
        {"role": "system", "content": "You are a helpful IT support agent. Use tools when needed."},
        {"role": "user", "content": user_text},
    ]

    for step in range(max_steps):
        resp = llm(messages=messages, tools=list(TOOLS.values()))
        # resp example:
        # {"type":"final","content":"..."} OR {"type":"tool_call","name":"search_kb","args":{...}}

        if resp["type"] == "final":
            return resp["content"]

        if resp["type"] == "tool_call":
            tool = TOOLS.get(resp["name"])
            if not tool:
                raise ToolDenied(f"Unknown tool: {resp['name']}")

            args = resp.get("args", {})
            validate_args(tool.input_schema, args)
            policy_check_tool_call(tool, args, ctx)

            result = tool.handler(args, ctx)

            # Append tool result to conversation state
            messages.append({"role": "assistant", "content": f"Calling tool {tool.name} with {args}"})
            messages.append({"role": "tool", "content": str(result)})
            continue

        raise RuntimeError(f"Unknown LLM response type: {resp['type']}")

    return "I couldn't complete the request within the allowed steps."

What to highlight in interviews:

  • strict schema validation
  • allowlists and approvals
  • bounded steps (prevents runaway loops)
  • tool results are appended as data, not paraphrased by the tool layer

Making It Distributed: Async Tool Execution with Durable State

For long-running or failure-prone tools (e.g., provisioning, ticket updates), use an event bus and workers. The orchestrator becomes a state machine.

sequenceDiagram
  participant C as Client
  participant O as Orchestrator
  participant Q as Queue
  participant W as Worker
  participant T as Tool Service
  participant M as Memory/DB

  C->>O: POST /agent/run
  O->>M: Load session state
  O->>O: Decide next action (LLM)
  O->>Q: Enqueue tool job (idempotency_key)
  O->>M: Persist "pending" step
  O-->>C: 202 Accepted + run_id

  Q->>W: Deliver job
  W->>T: Call tool (retry/backoff)
  T-->>W: Result
  W->>M: Persist result + audit log
  W->>Q: Ack

  C->>O: GET /agent/run/{run_id}
  O->>M: Read latest state
  O-->>C: Status + next message

Design notes:

  • Persist each step (inputs, tool calls, outputs) for replay and debugging.
  • Use idempotency_key per tool job to dedupe retries.
  • The client polls or uses WebSockets for updates.

Memory Architecture: RAG + Session Store + Summaries

A practical memory design:

flowchart TB
  subgraph Online
    S[Session Store\nRedis] --> O[Orchestrator]
    O --> SUM[Summarizer]
    SUM --> S
  end

  subgraph LongTerm
    P[(Postgres\nUser profile, tasks)]
    V[(Vector DB\nEmbeddings)]
    D[(Object Store\nDocs)]
  end

  O --> P
  O --> R[RAG Service]
  R --> V
  R --> D

Patterns that work well:

  • Keep raw transcripts in cheap storage (object store) for audit.
  • Keep a rolling summary per session for context efficiency.
  • Store structured memory (preferences, entities) separately from text.
  • For RAG, apply metadata filters (tenant, ACL, doc type) before similarity.

Guardrails Pattern: Policy Service + Enforcement Points

A common production setup is a dedicated policy/guardrail service used by:

  • API gateway (request filtering)
  • orchestrator (tool authorization)
  • worker (write-action gating)
  • output layer (redaction)
flowchart LR
  GW[Gateway] -->|input checks| POL[Policy Service]
  ORCH[Orchestrator] -->|tool authz| POL
  WORK[Workers] -->|write approval| POL
  ORCH -->|output checks| POL
  POL --> AUD[(Audit Log)]

Enforcement points:

  • Before LLM call: sanitize/annotate user input, inject policy context
  • Before tool execution: allowlist + argument validation + permission checks
  • After tool result: redact secrets before returning to model/user
  • Before final answer: require citations for factual claims when RAG is used

Example: Output Validation with JSON Schema

For interview scenarios, showing structured output is a strong signal. Example: require the agent to produce a ticket update object.

import json
import jsonschema

TICKET_UPDATE_SCHEMA = {
    "type": "object",
    "properties": {
        "ticket_id": {"type": "string"},
        "status": {"type": "string", "enum": ["open", "in_progress", "resolved"]},
        "summary": {"type": "string", "minLength": 1},
        "actions_taken": {"type": "array", "items": {"type": "string"}},
        "needs_human": {"type": "boolean"},
    },
    "required": ["ticket_id", "status", "summary", "actions_taken", "needs_human"],
    "additionalProperties": False,
}

def validate_agent_output(text: str) -> dict:
    data = json.loads(text)
    jsonschema.validate(data, TICKET_UPDATE_SCHEMA)
    return data

Why it matters: You can reject malformed outputs, route to fallback, or request regeneration—turning “probabilistic text” into “typed contracts.”

Best Practices

Reliability & Distributed Systems Practices

  1. Idempotency everywhere for writes

    • Tool handlers accept idempotency_key
    • Store dedupe keys in DB with TTL or permanent ledger
    • In interviews: mention at-least-once delivery and dedupe strategy

  2. Timeouts, retries, circuit breakers

    • Tools must have strict timeouts
    • Retries only for safe operations; use exponential backoff + jitter
    • Circuit-break failing dependencies to protect the agent’s SLO

  3. Durable execution traces

    • Persist step-by-step state: prompts, tool calls, results, decisions
    • Enables replay, debugging, offline evaluation, and compliance

  4. Bounded autonomy

    • Step limits, token budgets, tool-call budgets
    • Constrain the action space: fewer tools, narrower permissions

  5. Compensation (Saga) for multi-step writes

    • If step 3 fails after step 2 wrote data, run compensating action
    • Example: revoke token, rollback provisioning, reopen ticket

Security & Safety Practices

  1. Treat tool layer as a zero-trust boundary

    • Never execute arbitrary model-generated code
    • Use allowlists; deny by default
    • Validate args with schema; enforce tenant scoping

  2. Prompt injection resistance

    • Separate system/developer instructions from retrieved content
    • Mark retrieved documents as untrusted
    • Use retrieval filters + content scanning
    • Never allow retrieved text to redefine tool permissions

  3. Secrets management

    • LLM never sees raw secrets (API keys, tokens)
    • Use service-to-service auth (mTLS, IAM roles, short-lived tokens)
    • Redact sensitive tool outputs before adding to context

  4. Human approval gates

    • For destructive actions (delete, refund, password reset, prod deploy)
    • Include “why” and “diff” in approval request to reduce risk

Quality Practices (ML-Specific but System-Relevant)

  1. Grounding + citations

    • For factual answers, require citations from RAG results
    • If no citations, answer with uncertainty or ask clarifying questions

  2. Evaluation harness

    • Golden test sets: tool-call correctness, policy compliance, latency
    • Regression tests on prompts and tool schemas
    • Track tool-call success rates, hallucination reports, escalation rates

  3. Observability for agents

    • Distributed tracing across: request → LLM → tools → workers
    • Metrics: tool-call rate, refusal rate, approval rate, step count, cost
    • Logs: structured tool args (redacted), policy decisions, run IDs

Common Pitfalls to Avoid

  • Letting the LLM directly call tools without an enforcement layer
  • Overloading context with raw logs instead of summaries + structured state
  • No idempotency, causing duplicate writes during retries
  • RAG without ACL filters, leaking cross-tenant data
  • No fallbacks, leading to “agent stuck” loops and poor UX
  • No audit trail, making incidents impossible to investigate

Interview Relevance

How This Appears in System Design Interviews

Typical prompts:

  • “Design a customer support agent that can read KB articles and update tickets.”
  • “Design an incident response copilot that can run diagnostics and propose mitigations.”
  • “Design an enterprise agent that can access internal docs securely and take actions.”

Interviewers want to see:

  • A clear architecture (orchestrator, tools, memory, guardrails)
  • Reliability (async jobs, retries, idempotency, state persistence)
  • Security (least privilege, approvals, audit logs, ACL-aware retrieval)
  • Scalability (stateless services, queues, horizontal scaling)
  • Observability (tracing, metrics, replay)

Key Discussion Points (What to Say)

  1. Define boundaries

    • “The model proposes; the system disposes.”
    • Orchestrator enforces tool contracts and policies.

  2. Tooling strategy

    • Separate read-only tools (safe, sync) from write tools (risky, async + approval).
    • Use OpenAPI/JSON schema for typed tool calls.

  3. Memory strategy

    • Session store for short-term; Postgres for structured user/task memory; vector DB for semantic retrieval.
    • Summarize to control context size; keep raw logs for audit.

  4. Failure handling

    • At-least-once delivery; idempotency keys; dedupe store.
    • Timeouts, retries, circuit breakers; graceful degradation to “ask a human.”

  5. Guardrails

    • Policy service, allowlists, argument validation, PII redaction.
    • Human-in-the-loop for high-impact actions.

  6. SLOs and cost

    • Latency budgets: retrieval < 200ms, tool calls < 1s where possible.
    • Token/cost budgets; caching for common queries; batch embeddings.

A Strong Interview “Wrap-Up” Answer

If asked to summarize:
“I’d build a stateless orchestrator that runs a bounded agent loop. The LLM can request tool calls using typed schemas, but a policy layer validates and authorizes every action. Memory is layered: session state + summaries, structured long-term memory in SQL, and ACL-filtered RAG via a vector store. Long-running or risky tool calls go through a queue with idempotency and durable step logs for replay. Observability is end-to-end tracing across LLM and tools, and guardrails include prompt-injection defenses, redaction, and human approvals for write actions.”

Conclusion

Agentic AI systems are distributed systems with a probabilistic planner at the center. The difference between a demo and a reliable production agent is architecture: typed tool contracts, durable state, bounded execution, defense-in-depth guardrails, and strong observability.

For interviews, anchor your design in familiar system design principles—queues, idempotency, state machines, least privilege—then map LLM-specific risks (hallucinations, prompt injection, unsafe actions) to concrete mitigations (schemas, policy checks, approvals, citations, replayable traces). This framing demonstrates you can build agents that are not only capable, but also safe, scalable, and operable.

Tags

LLM Agents Agentic AI Tool Calling Retrieval-Augmented Generation (RAG) Distributed Systems Observability Safety Guardrails