Agentic Workflows: Building AI Agents for Enterprise Automation

AI agents that autonomously reason and take actions represent a fundamental shift from traditional automation. Unlike rule-based systems, agents can handle ambiguity, adapt to novel situations, and orchestrate complex multi-step workflows. But enterprise deployment requires careful attention to safety, reliability, and governance.

What Makes an Agent?

An AI agent combines several capabilities:

Reasoning: Understanding context and planning approaches Tool use: Executing actions in external systems Memory: Retaining context across interactions Iteration: Refining approaches based on feedback

The key distinction from chatbots: agents take actions that affect the real world.

Agent Architecture Patterns

ReAct (Reasoning and Acting)

The most common pattern interleaves thinking and doing:

Thought: I need to find the customer's order status
Action: query_orders(customer_id="12345")
Observation: [Order #789: shipped, tracking XYZ123]
Thought: I should check the tracking status
Action: track_shipment(tracking_number="XYZ123")
Observation: [In transit, estimated delivery tomorrow]
Thought: I have the information to respond
Action: respond_to_customer("Your order shipped and arrives tomorrow")

Pros: Simple, easy to debug, works well for straightforward tasks Cons: Can be inefficient for complex multi-step tasks

Plan and Execute

For complex tasks, separate planning from execution:

Planning phase:

Task: Generate quarterly sales report
Plan:
1. Query sales database for Q4 data
2. Aggregate by region and product category
3. Calculate YoY growth
4. Generate visualizations
5. Compile into report format

Execution phase: Execute each step, potentially re-planning if issues arise

Pros: Better for complex tasks, more predictable Cons: Planning may be wrong, requires re-planning capability

Multi-Agent Collaboration

Complex workflows benefit from specialized agents:

Researcher agent: Gathers information Analyst agent: Interprets data Writer agent: Produces documentation Reviewer agent: Validates outputs

Agents communicate through structured messages, each bringing specialized capabilities.

Pros: Separation of concerns, specialized optimization Cons: Coordination complexity, debugging difficulty

Tool-Augmented Generation

The simplest pattern: LLM decides which tools to call and how:

const tools = [
  {
    name: "search_knowledge_base",
    description: "Search internal documentation",
    parameters: { query: "string" }
  },
  {
    name: "create_ticket",
    description: "Create support ticket",
    parameters: { title: "string", description: "string", priority: "enum" }
  }
];

The LLM sees tool descriptions and decides which to invoke.

Tool Integration

Agents are only as useful as their tools. Tool design critically impacts agent effectiveness.

Tool Design Principles

Clear descriptions: The LLM reads tool descriptions to decide usage

// Bad: Ambiguous
{ name: "get_data", description: "Gets data" }

// Good: Specific
{ name: "get_customer_orders", description: "Retrieves order history for a customer by customer ID. Returns list of orders with status, date, and total amount." }

Robust input handling: Agents make mistakes; handle gracefully

function getCustomerOrders(customerId: string) {
  if (!customerId || typeof customerId !== 'string') {
    return { error: "customerId must be a non-empty string" };
  }
  // ... implementation
}

Informative outputs: Help the agent understand what happened

// Bad: Opaque
return { success: true }

// Good: Informative
return {
  success: true,
  ordersFound: 5,
  mostRecentOrder: { id: "789", status: "shipped", date: "2024-09-01" }
}

Tool Categories

Read-only tools: Search, query, retrieve—low risk Write tools: Create, update, delete—medium risk External actions: Send emails, charge cards, deploy code—high risk

Risk level should inform guardrail stringency.

Guardrails and Safety

Enterprise AI agents need multiple safety layers.

Input Validation

Prevent prompt injection and malicious inputs:

function validateInput(userInput: string): ValidationResult {
  // Check for injection patterns
  if (containsPromptInjection(userInput)) {
    return { valid: false, reason: "Suspicious input pattern" };
  }

  // Check length limits
  if (userInput.length > MAX_INPUT_LENGTH) {
    return { valid: false, reason: "Input too long" };
  }

  return { valid: true };
}

Output Filtering

Prevent sensitive data leakage:

function filterOutput(response: string): string {
  // Remove PII patterns
  response = redactEmails(response);
  response = redactPhoneNumbers(response);
  response = redactSSN(response);

  // Check against blocklist
  if (containsBlockedContent(response)) {
    return "I cannot provide that information.";
  }

  return response;
}

Action Confirmation

High-risk actions require explicit confirmation:

async function executeAction(action: AgentAction): Promise<ActionResult> {
  const riskLevel = assessRisk(action);

  if (riskLevel === 'high') {
    const confirmed = await requestHumanApproval(action);
    if (!confirmed) {
      return { status: 'cancelled', reason: 'Human rejected action' };
    }
  }

  return await performAction(action);
}

Audit Logging

Every agent action must be logged for compliance:

interface AuditLog {
  timestamp: Date;
  agentId: string;
  sessionId: string;
  action: string;
  inputs: Record<string, unknown>;
  outputs: Record<string, unknown>;
  userId: string;
  decision: string;
  reasoning: string;
}

Logs enable investigation, compliance reporting, and model improvement.

Human-in-the-Loop

Not everything should be automated. Strategic human involvement improves outcomes.

When to Involve Humans

High-stakes decisions: Actions with significant financial or legal impact Ambiguous situations: When the agent isn't confident Exception handling: Situations outside normal parameters Quality assurance: Sampling outputs for accuracy

Effective Human Checkpoints

Design checkpoints that are actionable:

interface HumanReviewRequest {
  summary: string;           // What the agent wants to do
  reasoning: string;         // Why the agent chose this action
  alternatives: string[];    // Other options considered
  confidence: number;        // Agent's self-assessed confidence
  requiredDecision: string;  // What the human needs to decide
  deadline: Date;            // When decision is needed
}

Feedback Loops

Human corrections improve future agent behavior:

Human corrects agent decision
Correction logged with context
Corrections analyzed for patterns
Prompts or fine-tuning updated
Repeat

Enterprise Deployment Considerations

Scalability

Agent architectures must handle enterprise load:

Queue-based processing for async workflows
Horizontal scaling of agent workers
Rate limiting for external API calls
Caching for repeated operations

Reliability

Agents must handle failures gracefully:

Retry logic with exponential backoff
Fallback to simpler approaches
Circuit breakers for failing dependencies
State persistence for long-running workflows

Cost Management

LLM calls are expensive:

Cache common queries
Use smaller models for simple decisions
Batch operations where possible
Monitor and alert on cost anomalies

Key Takeaways

Start with narrow scope: Begin with well-defined, low-risk workflows
Design tools carefully: Tool quality determines agent effectiveness
Layer safety measures: Multiple guardrails prevent failures
Keep humans in the loop: Strategic involvement improves outcomes
Log everything: Compliance and improvement require visibility
Plan for failure: Agents will make mistakes; design for graceful handling
Measure and iterate: Track success rates, failure modes, and costs