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AI Agents in Production Workflows

· 4 min read
Suraj Srivastav
Suraj Srivastav
Engineering Leader & Systems Architect

AI agents are moving from research labs to production systems. But deploying agents at scale is fundamentally different from deploying traditional applications. Let me share what we've learned.

What Are AI Agents?​

An AI agent is a system that can:

  • Perceive its environment
  • Make decisions based on that perception
  • Take actions to achieve goals
  • Learn from feedback

Unlike traditional chatbots that respond to user input, agents can:

  • Plan multi-step workflows
  • Use tools and APIs
  • Recover from failures
  • Improve through feedback

The Challenge: Reliability at Scale​

Agents introduce new reliability challenges:

1. Non-Determinism​

Traditional code is deterministic. Feed the same input, get the same output. Agents are not.

Solution: Design for variability

  • Add explicit error handling
  • Implement retry logic with backoff
  • Use deterministic fallbacks when agents fail
  • Monitor agent decisions for anomalies

2. Hallucinations​

LLMs can confidently generate incorrect information.

Solution: Grounding and verification

  • Ground agents in your actual data
  • Verify agent decisions against trusted sources
  • Use agent feedback to improve prompts
  • Log decision chains for auditing

3. Cost Control​

Running agents at scale gets expensive fast.

Solution: Efficient agent design

  • Use smaller models for simple tasks
  • Cache decisions when appropriate
  • Batch similar requests
  • Monitor token usage and costs

Architecture Pattern: The Control Loop​

Here's a pattern that works well in production:

[User Request]
    ↓
[Agent Planner] → [Tool Calls] → [Tool Execution]
    ↓                               ↓
    ←────────── [Reflection] ←─────
    ↓
[Verification] → Pass? → [Action]
    ↓
   Fail? → [Recovery Strategy]

Key Components:​

Planner: The agent plans what to do. Be explicit about constraints.

Tools: Agents execute via tools. Make tools atomic and well-defined.

Reflection: After execution, agents reflect on results. This improves decisions.

Verification: Always verify critical decisions. Don't blindly trust agent output.

Recovery: When things fail, have explicit recovery strategies.

Real Example: Customer Support Agent​

We built an agent to handle customer support tickets:

What it does:

  • Reads incoming support tickets
  • Decides if it can resolve or needs escalation
  • Queries knowledge base and database
  • Drafts responses
  • Escalates complex issues

How we made it reliable:

  1. Grounding: Agent only uses our knowledge base + database
  2. Guardrails: Hard limits on what agent can do (no payments, no data deletion)
  3. Verification: Manager reviews all responses before sending
  4. Feedback loop: Bad decisions are logged and used for fine-tuning
  5. Escalation: Complex issues go to humans immediately

Results:

  • 60% of tickets resolved automatically
  • Resolution time dropped 40%
  • Customer satisfaction: 4.2/5
  • Human time freed for complex issues

Implementation Patterns​

Pattern 1: Tool-Based Agents​

Agents call tools (functions, APIs) to accomplish tasks.

# Tools the agent can call
tools = [
    lookup_customer,
    query_database,
    send_email,
    escalate_to_human
]

# Agent decides which tools to use
agent = create_agent(tools=tools, constraints=[
    "Do not handle payments",
    "Always escalate legal issues",
    "Verify before sending emails"
])

Pattern 2: Workflow Agents​

Agents orchestrate workflows where order matters.

workflow = [
    {"step": "validate_input", "agent": input_validator},
    {"step": "process_request", "agent": processor},
    {"step": "verify_output", "agent": verifier},
    {"step": "execute_action", "agent": executor},
]

Pattern 3: Hierarchical Agents​

One agent delegates to specialized agents.

[Manager Agent]
    ├─ [Analyst Agent] - analyzes data
    ├─ [Writer Agent] - writes output
    └─ [Reviewer Agent] - reviews quality

Monitoring Agents​

You need different metrics for agents:

  • Decision quality: Are agent decisions correct?
  • Coverage: What percentage of tasks does the agent handle?
  • Escalation rate: What % needs human intervention?
  • Cost per task: Tokens used × cost
  • Latency: How long does the agent take?
  • Hallucination rate: How often does it generate false info?

The Future​

Agents will become standard infrastructure for many workflows. But they require:

  • Explicit error handling
  • Verification and guardrails
  • Careful monitoring
  • Human oversight for critical decisions

The key is treating agents as tools that augment humans, not replace them.

Start small. Deploy agents for low-risk, high-value tasks. Learn from production data. Scale gradually.