The demo looks magical. An AI agent that can browse the web, query databases, write code, and compose emails - all from a single natural language prompt. The CEO is impressed. The budget is approved.

Six months later, the project is quietly shelved.

This pattern repeats across enterprises globally, and we’ve seen it play out dozens of times in Indian organizations over the past year. The gap between “agent demo” and “agent in production” is wider than most teams anticipate.

The Autonomy Trap

The fundamental mistake is treating agent autonomy as a feature rather than a risk.

When you give an LLM the ability to take actions - calling APIs, writing to databases, sending communications - you’re not just adding capabilities. You’re creating a system that can fail in ways that compound.

Consider a simple customer service agent:

User: "Cancel my order and refund me"
Agent: [Looks up order] -> [Cancels order] -> [Initiates refund] -> [Sends confirmation]

What happens when the agent cancels the wrong order? Or initiates a refund for an amount the customer didn’t pay? Or sends a confirmation email to the wrong address?

Each action is a potential failure point. And unlike a traditional software bug that produces the same wrong output consistently, agent failures are often non-deterministic. The same input might work 99 times and fail catastrophically on the 100th.

The Real Production Challenges

After deploying agentic systems for banking, logistics, and government clients, we’ve identified the failure modes that don’t show up in demos:

1. Context Window Degradation

Agents accumulate context as they work. By the time an agent has made several tool calls, its context window is polluted with:

  • API responses (often verbose JSON)
  • Error messages from failed attempts
  • Intermediate reasoning that’s no longer relevant

This degrades decision quality over time. An agent that performs well on simple tasks starts making poor choices as complexity increases.

graph LR
    A[Initial Prompt] --> B[Tool Call 1]
    B --> C[Response 1 - 2KB]
    C --> D[Tool Call 2]
    D --> E[Response 2 - 5KB]
    E --> F[Tool Call 3]
    F --> G[Response 3 - 8KB]
    G --> H[Decision Quality Drops]

    style H fill:#ff6b6b

2. Error Recovery is Harder Than It Looks

When a traditional API call fails, you retry with exponential backoff. When an agent action fails, what do you do?

The agent needs to:

  1. Recognize the failure (not always obvious from API responses)
  2. Understand why it failed
  3. Decide whether to retry, try an alternative, or escalate
  4. Maintain consistency with any partial state changes

Most agent frameworks punt on this problem. They either retry blindly (causing duplicate actions) or fail completely (frustrating users).

3. The Evaluation Gap

How do you test an agent? Traditional software testing assumes deterministic outputs. Agent outputs vary based on:

  • Model temperature and sampling
  • Exact prompt phrasing
  • Order of information in context
  • Time of day (if using real-time data)

We’ve seen teams ship agents with test suites that pass 95% of the time in CI/CD but fail 30% of the time in production - because production queries are messier than test cases.

What Actually Works

Based on our experience building Orchestrate, our multi-agent platform, here’s what we’ve found makes agents production-ready:

Principle 1: Orchestration Over Autonomy

Don’t give agents free rein. Define explicit workflows with clear decision points.

flowchart TD
    A[User Request] --> B{Intent Classification}
    B -->|Order Query| C[Order Agent]
    B -->|Refund Request| D[Refund Agent]
    B -->|General Query| E[Support Agent]

    C --> F{Confidence > 0.9?}
    F -->|Yes| G[Execute Action]
    F -->|No| H[Human Review Queue]

    D --> I{Amount > 10K?}
    I -->|Yes| H
    I -->|No| J[Auto-Process]

    G --> K[Audit Log]
    J --> K
    H --> K

The agent handles the cognitive work - understanding intent, gathering information, making recommendations. But the workflow controls when actions actually execute.

Principle 2: Stateless Agents, Stateful Orchestration

Individual agents should be stateless. They receive a focused context, perform a specific task, and return a result.

State lives in the orchestration layer:

  • What has been attempted
  • What has succeeded or failed
  • What the user’s session looks like
  • What actions are pending approval

This separation makes debugging tractable. When something goes wrong, you can examine the orchestration state without reconstructing an agent’s entire reasoning chain.

Principle 3: Graduated Autonomy

Not all actions carry equal risk. A well-designed system grants autonomy proportional to reversibility:

Action Type Risk Level Autonomy
Read data Low Full autonomy
Draft response Low Full autonomy
Send internal notification Medium Auto-execute with logging
Modify customer record High Require confirmation
Financial transaction Critical Human approval required

This isn’t about trusting the AI less - it’s about building systems that fail gracefully.

Principle 4: Observable by Default

Every agent action should produce structured telemetry:

@trace_agent_action
async def process_refund(order_id: str, amount: Decimal) -> RefundResult:
    """
    Telemetry captured automatically:
    - Input parameters
    - Model calls and responses
    - Tool invocations
    - Latency breakdown
    - Confidence scores
    - Final outcome
    """
    # ... implementation

When a customer complains about a wrong refund, you need to reconstruct exactly what happened. “The AI made a mistake” isn’t an acceptable answer for your compliance team - or for RBI auditors.

This is why we built comprehensive audit trails into Guardian, our AI reliability monitoring platform. Every decision is traceable.

The Indian Enterprise Context

Deploying agents in Indian enterprises adds specific challenges:

Language mixing: Users switch between English, Hindi, and regional languages mid-conversation. An agent trained primarily on English struggles with “Mera order cancel karo and refund de do.”

Infrastructure variability: Agents calling internal APIs might face 500ms latency in Mumbai and 3 seconds in a Tier-2 city office. Timeout handling needs to be robust.

Regulatory requirements: RBI’s guidelines on AI in banking require explainability for customer-impacting decisions. DPDP Act requires data minimization. Your agent architecture needs to accommodate these from day one - not as an afterthought.

A Realistic Assessment

AI agents are genuinely useful for:

  • Information synthesis: Gathering data from multiple sources and summarizing
  • Draft generation: Creating first versions of documents, emails, reports
  • Workflow assistance: Guiding users through complex processes
  • Anomaly detection: Flagging unusual patterns for human review

AI agents are risky for:

  • Unsupervised financial transactions: Too many edge cases
  • Customer communications without review: Tone and accuracy issues
  • Complex multi-step processes: Compounding error probability
  • Anything requiring legal accountability: Who’s responsible when it goes wrong?

Getting Started Right

If you’re building an agentic system, here’s our recommended approach:

  1. Start with a single, well-defined workflow. Don’t build a general-purpose agent. Build a “refund processing assistant” or “document verification agent.”

  2. Instrument everything from day one. You’ll thank yourself when debugging production issues.

  3. Design for human-in-the-loop. Make it easy to escalate, review, and override. The goal isn’t full automation - it’s augmented efficiency.

  4. Test with adversarial inputs. What happens when users try to confuse the agent? When they provide incomplete information? When they change their mind mid-conversation?

  5. Plan for model changes. Your agent will need to work with different models over time. Abstract the model layer so you can swap without rebuilding.

We’ve spent the last year building Orchestrate specifically to address these challenges - reliable agent orchestration with enterprise governance built in. If you’re struggling with agent deployment, let’s talk.

The future of enterprise AI is agentic. But getting there requires engineering discipline, not just prompt engineering.