Building Production-Ready AI Agents: From Prototype to Scale

Dekode Team
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Building Production-Ready AI Agents: From Prototype to Scale

It has never been easier to build an impressive AI demo. With a few lines of Python, an OpenAI API key, and a framework like LangChain, you can spin up a "chatbot" that reasons, retrieves data, and answers questions.

It has also never been harder to build a product.

The gap between "it works on my machine" and "it works for 10,000 users with 99.9% reliability" is a chasm filled with hallucinations, latency spikes, infinite loops, and prompt injection vulnerabilities. For CTOs and engineering leaders, the challenge isn't generating tokens—it's engineering reliability into a probabilistic system.

In this post, we explore the architecture, stack, and hard-won lessons of building production-ready AI agents.

Executive Summary

  • Probabilistic Engineering: Unlike traditional software, AI systems are non-deterministic. Your engineering practices must shift from "asserting truth" to "evaluating probability."
  • The "Agent" Spectrum: Not everything needs to be an autonomous agent. Most value today comes from "Augmented Workflows"—deterministic code with AI calls at specific decision nodes.
  • Evals are Unit Tests: You cannot deploy what you cannot measure. An automated evaluation pipeline (using LLMs to judge LLMs) is non-negotiable for production.
  • Observability is Critical: You need to trace execution not just by latency, but by token cost, retrieval quality, and intermediate reasoning steps.
  • Governance & Safety: Guardrails must be architectural components, not just "be nice" system prompts.

The "It Works on My Machine" Trap

We often see teams stall at the "90% accuracy" mark. The prototype looks great: it answers simple questions about documentation perfectly. But when exposed to real users, edge cases explode:

  • Users ask ambiguous questions, and the agent confidently hallucinates an answer.
  • The RAG (Retrieval-Augmented Generation) pipeline retrieves irrelevant chunks, confusing the model.
  • A user tries to trick the bot into ignoring its instructions (jailbreaking).

The transition to production requires a shift in mindset. We are no longer just writing code; we are orchestrating a system where the "CPU" (the LLM) is creative, unpredictable, and occasionally dishonest.

The Modern AI Stack

To tame this complexity, we need a robust stack. It's no longer just app.py calling openai.ChatCompletion.

1. The Orchestration Layer

This is the "brain" logic. While frameworks like LangChain or AutoGen are popular for prototyping, in production, we often see teams migrating to lighter, more controllable abstractions—or even vanilla code. The key is control. You need to know exactly when the model is called, with what context, and how errors are handled.

2. The Context Engine (RAG)

Context is the fuel for your agents. A vector database (like Pinecone or Weaviate) is standard, but production RAG goes deeper:

  • Hybrid Search: Combining vector similarity (semantic search) with keyword search (BM25) for precision.
  • Reranking: Using a specialized model (like Cohere Rerank) to sort retrieved chunks before sending them to the LLM.
  • Metadata Filtering: Ensuring a user only searches their documents.

3. The Model Gateway

Directly calling model APIs is risky. A gateway layer (like LiteLLM or a custom proxy) provides:

  • Fallback: If OpenAI is down, switch to Anthropic or Azure.
  • Cost Tracking: Tagging requests by user, feature, or team.
  • Rate Limiting: Protecting your budget and upstream limits.

4. The Evaluation & Observability Platform

This is the newest and most critical layer. Tools like LangSmith or Arize Phoenix allow you to "replay" traces, score them against ground truth, and spot regressions before your users do.

Architecture Patterns: Agents vs. Chains

A common mistake is trying to make everything an autonomous agent.

Chains (Pipelines) are sequences of steps: A -> B -> C. They are reliable and easy to debug. Use these for 80% of your use cases, such as summarization, extraction, or classification.

Agents use an LLM as a reasoning engine to determine the steps: "I need to check the weather, then email the user." The model decides if it needs to check the weather. This is powerful but fragile. It requires:

  • Planning: The ability to break a complex goal into sub-tasks.
  • Memory: Keeping track of past actions to avoid loops.
  • Tool Use: Clean interfaces for the model to call APIs (calculators, database lookups).

Recommendation: Start with Chains. Only introduce Agentic behavior when the workflow is too dynamic to be hard-coded.

The Three Pillars of Reliability

If you take one thing away from this article, let it be this: You cannot optimize what you cannot measure.

1. Evals (Evaluations)

An "Eval" is a test case for an AI. It consists of:

  • Input: "How do I reset my password?"
  • Expected Output: "Go to settings > security..."
  • Grading Logic: Did the actual output match the expected output?

In the past, humans graded these. Now, we use stronger models (like GPT-4o) to grade the outputs of smaller, faster models. This is "LLM-as-a-Judge."

  • Code Example: A simple eval might check if the answer mentions "Settings."
  • Semantic Example: An eval might ask GPT-4, "Is this answer helpful and polite?"

2. Observability

When an agent fails, you need to know why. Did the retrieval fail? Did the model hallucinate? Did the tool return an error? Production tracing allows you to visualize the entire tree of execution. You should be able to click on a failed session and see:

  • The exact prompt sent to the LLM.
  • The context retrieved from the vector DB.
  • The raw tool outputs.

3. Guardrails

Guardrails are the safety net. They sit between the user and the model, and between the model and the output.

  • Input Guardrails: Detect PII (Personally Identifiable Information), toxic language, or prompt injection attempts before they reach the model.
  • Output Guardrails: Scan the response to ensure it doesn't contain competitor names, hallucinated URLs, or harmful advice.
  • Syntactic Guardrails: Force the model to output valid JSON (using tools like Pydantic or instructor) so your code doesn't crash.

Case Study: A Tier-1 Support Agent

Let's look at a real-world scenario: A "Tier 1" support agent that can answer questions and perform simple actions like checking order status.

The Workflow:

  1. Classification: The user query comes in. A specialized, fast model (like GPT-3.5-Turbo or a fine-tuned Llama 3) classifies the intent: Question, Order_Status, or Escalate.
  2. Routing:
    • If Question: Trigger the RAG pipeline. Retrieve docs, rerank, and generate an answer.
    • If Order_Status: The agent extracts the Order ID. It calls the Shopify_API tool. If the Order ID is missing, it asks the user for it (this is the "agentic" loop).
    • If Escalate: Hand off to a human agent via Intercom/Zendesk API.
  3. Verification: The generated answer is passed through a "Self-Correction" step where a second model quickly checks: "Does this answer rely only on the provided context?"
  4. Response: The final answer is streamed to the user.

The Ops:

  • Every night, we run a regression suite of 500 past customer queries.
  • If the "Hallucination Rate" (measured by an evaluator model) spikes above 2%, the deployment is blocked.
  • We monitor the "Escalation Rate." If it drops too low, the bot might be over-confident. If it's too high, the bot is useless.

The "Unsexy" Parts: Governance & Cost

Scaling agents isn't just about intelligence; it's about logistics.

  • Caching: LLM calls are slow and expensive. Semantic caching (storing the embedding of a query and its response) can reduce costs by 30-50% for frequently asked questions.
  • Rate Limiting: One malicious user can drain your API quota. Implement strict per-user limits.
  • Data Privacy: If you are SOC2 or HIPAA compliant, you must ensure that user data isn't effectively "trained" into the model or logged in plain text in a third-party observability tool. Use data masking / redaction middleware.

Roadmap to Production

Don't try to build "Jarvis" on day one.

  1. Crawl (Internal Copilot): Build a tool for your own support team. It suggests answers that the human approves. This gathers high-quality data and feedback with zero customer risk.
  2. Walk (Human-in-the-Loop): Deploy the bot to customers, but with a "low confidence" threshold. If the bot isn't 95% sure, it silently routes to a human.
  3. Run (Autonomous Agent): Allow the bot to handle end-to-end tasks for specific, well-scoped intents (e.g., "Reset Password", "Track Order").

Conclusion: The Future of Human + AI

The goal of AI engineering isn't to replace humans, but to elevate them. By offloading the retrieval, synthesis, and rote execution tasks to agents, we free up our teams to focus on high-leverage problem solving.

Building these systems requires a new discipline—a blend of software engineering, data science, and product intuition. It is messy, probabilistic, and incredibly exciting.

At DeKode, we help engineering teams bridge this gap. Whether you need a custom RAG architecture, a multi-agent orchestrator, or a production-grade eval suite, we build the infrastructure that makes AI reliable.

References & Further Reading