DEV Community

Kuldeep Paul
Kuldeep Paul

Posted on

What You’re Getting Wrong When Building AI Applications in 2025

#ai #testing #devops #llm

AI applications crossed the chasm from prototypes to production systems. Yet many teams still treat large language models (LLMs), RAG pipelines, and voice agents as black boxes—with brittle prompts, shallow testing, and little post-deployment accountability. In 2025, the organizations shipping reliable, trustworthy AI share one consistent pattern: they instrument, evaluate, and simulate their agents end-to-end before, during, and after release. This piece breaks down the common missteps and lays out a practical path to AI reliability—grounded in industry guidance and technical best practices—with concrete ways to fix them using Maxim AI’s full-stack platform.

The Biggest Misconceptions: What Fails in Production

1) Assuming model quality equals application quality

LLMs are non-deterministic. Application quality depends on the entire workflow—prompt design, retrieval, tool usage, guardrails, latency, and cost—under realistic user scenarios. The NIST AI Risk Management Framework (AI RMF) calls for systematic lifecycle controls encompassing governance, measurement, and continuous risk management across socio-technical systems, not just models. See the framework overview and guidance: AI Risk Management Framework | NIST and the full specification: AI RMF 1.0 (PDF).

What to do instead:

  • Treat prompts, RAG retrieval, and agent tools as components that need observability, evals, and simulations—not static configuration.
  • Quantify quality under variable conditions (input entropy, ambiguous intents, noisy retrieval, tool failures).

2) Overfitting to internal test sets; under-testing real conversations

Most teams rely on small synthetic test sets or handpicked cases. That misses emerging behaviors and rare failures that only appear in multi-turn trajectories and diverse personas. RAG pipelines particularly suffer when chunking, embeddings, and reranking choices do not reflect actual query distribution. A comprehensive survey cataloging RAG evaluation dimensions (retrieval relevance, comprehensiveness, generation faithfulness, end-to-end correctness) underscores why component-level and system-level evals are both required: RAG Evaluation Survey. For empirical best practices on chunk sizes, hybrid retrieval, and reranking combinations, see: Searching for Best Practices in RAG (Paper) and Maxim’s applied guidance: RAG best practices.

What to do instead:

  • Run agent simulation across hundreds of scenarios and user personas; evaluate conversational trajectories, completion success, and failure points. Maxim’s simulation and evaluation suite is designed for this: Agent Simulation & Evaluation.

3) Ignoring the OWASP Top 10 for LLM applications

Security gaps—prompt injection, insecure output handling, supply chain vulnerabilities—remain the fastest path to breach and reputational damage. Teams often bolt-on basic filters but fail to harden agent workflows and plugins against indirect injection or excessive agency. Reference the latest community guidance: OWASP Top 10 for LLM Applications. In 2025, research continues to evolve defenses beyond ad-hoc prompting; for example, optimizing defenses that preserve utility at test time: Defending Against Prompt Injection With DefensiveTokens.

What to do instead:

  • Apply security-by-design for agent tools, enforce least privilege, validate outputs before execution, and instrument ai observability to detect anomalies in real time.
  • Track and evaluate for hallucination detection, excessive agency, and sensitive information disclosure alongside reliability metrics.

4) Treating observability as basic logs

Traditional logging cannot explain failures in multi-service agent workflows. You need distributed tracing across sessions, traces, spans, generations, tool calls, and retrieval steps, coupled with automated evals on live traffic. Observability must connect quality signals with cost, latency, and routing behavior to inform prompt management and agent debugging decisions.

What to do instead:

  • Instrument your application with Maxim’s agent observability to trace every LLM and RAG step, correlate quality and performance, and alert on degradations: Agent Observability.

5) Hardwiring to a single provider without routing or failover

Production reliability and cost management require a model router/llm gateway with automatic fallbacks, load balancing, and semantic caching. In practice, response quality, throughput, and price vary across providers, regions, and times of day.

What to do instead:

A Practical Blueprint: Simulation, Evaluation, and Observability

Pre-release: Build quality in before shipping

  • Experimentation and prompt engineering: Version prompts, compare model outputs, and optimize parameters with Playground++ to triage quality versus cost and latency. See prompt versioning and deployment capabilities: Experimentation (Playground++).
  • Data curation: Build multi-modal datasets and evolve them from logs and human feedback. This supports ai evaluation, rag evaluation, and later fine-tuning.
  • Agent simulation: Validate multi-turn trajectories across personas with agent simulation; reproduce issues from any step and check task completion with agent tracing and agent debugging workflows: Agent Simulation & Evaluation.
  • Unified evals: Mix statistical, programmatic, and LLM-as-a-judge evaluators; run llm evals, rag evals, voice evals, and copilot evals at session, trace, or span granularity. Configure human-in-the-loop last-mile checks to align to preference: Agent Simulation & Evaluation.

Keywords to anchor practice: ai evals, llm evaluation, agent evaluation, rag evaluation, voice evaluation, ai reliability, trustworthy ai.

Production: Instrument for reliability, cost, and scale

  • Agent observability: Continuously monitor ai quality via llm observability metrics, model drift, hallucination signals, and task success. Track tokens, latency, and cost per trace, including retrieval steps for rag tracing and voice tracing in voice agents. See features: Agent Observability.
  • Automated in-production evals: Attach custom rules and evaluators to live logs. Measure prompt engineering changes, model router behavior, and llm gateway routes with quality checks and alerts.
  • Gateway resilience: Route requests with policy-driven llm router rules, enforce budgets, and audit with governance. Bifrost’s Model Context Protocol (MCP) integrates tools safely, while semantic caching reduces cost and tail latency: MCP, Semantic Caching.

Continuous improvement: Close the loop

  • Prompt management and versioning: Maintain structured histories and prompt versioning to compare behaviors across agents and deployments: Experimentation (Playground++).
  • Datasets from production: Curate evaluation and fine-tuning datasets with real user sessions and agent monitoring insights; replay traces in simulation to regression-test fixes: Agent Observability and Agent Simulation & Evaluation.

How Teams Fall Short: A Diagnostic Checklist

Use this checklist to find and fix quality bottlenecks fast.

  • No end-to-end tracing: If you cannot attribute a failure to a specific span (prompt, retrieval, tool call), debugging llm applications will be slow. Implement llm tracing and ai tracing with Maxim’s SDKs: Agent Observability.
  • Sparse or static evals: If you only evaluate offline on narrow sets, you will miss real-world error modes. Move to custom evaluators at trace/span and incorporate human-in-the-loop for nuanced judgments: Agent Simulation & Evaluation.
  • Unhardened RAG: If chunk sizes, embeddings, and reranking are not tuned to your domain, expect rag monitoring to show poor grounding and high hallucination. Apply best practices and validate with rag observability and rag tracing: RAG best practices.
  • Single-provider fragility: If your app depends on one provider without automatic fallbacks, reliability will suffer during incidents or quota limits. Use Bifrost’s ai gateway for load balancing and failover: Fallbacks & Load Balancing.
  • Voice agent blind spots: If voice agents lack voice observability and voice monitoring on transcripts, prosody, and intent, hidden regressions persist. Evaluate voice agents with voice evals and simulate user contexts: Agent Simulation & Evaluation.

Building Trustworthy AI: Security, Governance, and Accountability

Trust is not a banner; it is measurable practice.

Why Maxim AI Stands Out for Cross-Functional Teams

Maxim provides a full-stack offering for multimodal agents—from Experimentation, Simulation, Evaluation, to Observability—designed for AI engineers and product teams to collaborate without friction.

A 30-Day Action Plan: From brittle to reliable

  • Week 1: Instrument traces and spans; stand up agent observability with alerts for latency, cost, and quality rules. Connect to Bifrost for multi-provider access, fallbacks, and budgets.
  • Week 2: Establish evals (LLM-as-judge + programmatic) at session/trace/span; curate datasets from production logs for rag evals, agent evals, and voice evals.
  • Week 3: Run agent simulation across personas; reproduce defects via agent tracing and re-runs from any step; resolve prompt engineering issues with versioned comparisons.
  • Week 4: Harden security against OWASP Top 10 categories; validate governance against NIST AI RMF controls; align ai reliability metrics with product KPIs.

The result: measurable gains in task completion, reduced hallucination rates, controlled cost/latency, and faster incident response—across voice agents, RAG-centric chatbots, and copilot-style workflows.

To see Maxim AI in action, request a hands-on walkthrough: Maxim AI Demo. Or start building with our platform today: Sign up to Maxim.

Top comments (0)