Production AI Reliability

AI Observability and Monitoring Services
See Why Your AI System Behaves the Way It Does

Devlyn builds observability for production LLM applications, RAG systems, copilots, agents, extraction workflows, and model-powered products. We instrument prompts, traces, retrieval, tool calls, evaluations, cost, latency, model versions, user feedback, incidents, and quality signals so engineering teams can debug behavior, detect regressions, control spend, and improve AI reliability with evidence.

Review our AI observability gaps See monitoring scope

End-to-end traces

Prompts, retrieval, tools

Evaluation pipelines

Quality and regressions

AI incident playbooks

Alerts, owners, fixes

AI failures are hard to fix when the system only logs HTTP requests

Traditional observability tells you whether a service was slow or returned an error. Production AI needs another layer: what prompt ran, what context was retrieved, which model responded, which tool was called, what changed since the last release, and whether the output was actually useful.

What breaks

A user reports a bad answer, but the team cannot reconstruct the prompt, retrieved context, model version, tool calls, user state, or downstream action that produced it.

A model or prompt update ships successfully from a software perspective, but answer quality, groundedness, tone, extraction accuracy, or task completion quietly regresses.

RAG systems retrieve irrelevant chunks, stale content, or too much context, but the problem is hidden inside a single application log line.

Agent workflows fail because of loop behavior, retries, tool errors, missing permissions, unsafe arguments, or unclear approval state, not because the API endpoint failed.

AI spend grows across features, tenants, users, agents, or providers, but cost telemetry is not connected to quality, latency, or product usage.

How Devlyn reduces risk

We design trace schemas that capture prompts, model calls, retrieval, embeddings, reranking, tool calls, agent steps, latency, token use, errors, user feedback, and business context.

We build evaluation workflows that test AI quality before release and monitor sampled production outputs after release using golden datasets, human review, automated checks, and calibrated judge models where appropriate.

We create dashboards that separate infrastructure health from AI behavior: retrieval quality, answer quality, cost, latency, refusal rates, hallucination signals, extraction accuracy, and tool success.

We connect alerts to incident playbooks so teams know who owns a regression, how to triage it, what to roll back, and what evidence to preserve.

We choose tools based on your architecture, hosting constraints, data sensitivity, team workflow, and existing observability stack instead of forcing a single vendor.

What we deliver in AI observability and monitoring

The service creates a practical operating layer for AI reliability. Your team should be able to inspect what happened, compare behavior across versions, measure quality, and act when the system degrades.

01

Trace and span instrumentation

Capture LLM calls, prompts, system instructions, retrieval steps, embeddings, rerankers, tool calls, agent transitions, custom logic, errors, and latency with useful attributes.

02

Prompt and model version tracking

Connect behavior to prompt versions, model names, provider settings, temperature, tools, schemas, retrieval configuration, deployment environment, and release history.

03

Evaluation pipeline design

Create offline regression tests, online sampled evaluations, golden datasets, annotation queues, custom scoring, user feedback loops, and quality dashboards.

04

RAG quality monitoring

Track retrieval relevance, citation coverage, context size, source freshness, chunk quality, reranker behavior, grounding, missing-answer patterns, and retrieval cost.

05

Agent monitoring and incident reconstruction

Observe tool-call chains, loop depth, retries, permissions, approval state, memory use, downstream writes, failure causes, and evidence needed to reconstruct an agent action.

06

Dashboards, alerts, and playbooks

Build dashboards and alert rules for quality, cost, latency, drift, safety events, tool failures, provider issues, and release regressions with owner-ready runbooks.

The monitoring layers production AI needs

A useful AI observability layer does not stop at model calls. It connects model behavior to product context, data context, system reliability, cost, and user outcomes.

Model and prompt layer

Track model provider, model version, prompt version, system instructions, parameters, schema constraints, token usage, refusals, safety events, and output format failures.

Retrieval and knowledge layer

Track query rewriting, embeddings, vector search, filters, chunk selection, reranking, source freshness, citation quality, context payload, and answer grounding.

Agent and tool layer

Track plans, tool calls, tool arguments, permissions, retries, loop depth, approval points, downstream actions, memory state, and handoff decisions.

Evaluation and quality layer

Track task success, extraction accuracy, groundedness, factuality, completeness, tone, safety, human review, user feedback, and regression signals.

Cost and latency layer

Track tokens, provider spend, cache hits, request volume, queue time, model latency, retrieval cost, tool latency, and cost per workflow or completed task.

Incident and governance layer

Track alerts, owners, rollback decisions, evidence retained, model-card updates, risk-register updates, customer-impact notes, and post-incident actions.

Tooling options we can work with

The right stack depends on your framework, data sensitivity, hosting model, budget ownership, and whether you need traces, prompt management, evaluations, gateways, or enterprise APM integration.

Useful when teams want open-source LLM tracing

prompt management

evaluations

sessions

cost and latency dashboards

self-hosting options

OpenTelemetry compatibility

OpenTelemetry compatibility

Useful for LangChain and LangGraph-heavy teams that want tracing

debugging

evaluation

evaluation

monitoring

prompt workflow support inside the LangChain ecosystem

Useful when teams want open-source experimentation

tracing

evaluation

evaluation

troubleshooting

OpenTelemetry or OpenInference instrumentation

OpenTelemetry or OpenInference instrumentation

local or managed deployment options

Useful when model traffic should pass through a gateway for logging

routing

rate limits

caching

model-provider abstraction

cost analytics

Useful when teams already rely on Datadog

New Relic

New Relic

Grafana

Grafana

OpenSearch

or other telemetry systems and need GenAI semantic conventions or custom spans

Useful when privacy

volume

retention

or product-specific diagnostics require a custom event schema

warehouse sink

dashboard layer

or review workflow

How the AI observability engagement runs

We start by defining what must be observable, then instrument the highest-risk workflows first. The goal is to make debugging and improvement faster without collecting sensitive data unnecessarily.

We identify the AI features, agents, RAG paths, extraction tasks, model calls, downstream systems, customer impact, and incidents that need visibility first.
Map critical AI workflows
We define fields for prompts, versions, retrieval, tools, costs, latency, users, tenants, quality labels, errors, privacy rules, and retention boundaries.
Design trace and event schemas
We add SDK, gateway, OpenTelemetry, or custom instrumentation and route data to the selected observability platform, warehouse, or APM stack.
Instrument and route telemetry
We create quality checks, regression datasets, production-sample scoring, review queues, cost-quality dashboards, trace views, and product-level reports.
Build evals and dashboards
We define thresholds, anomaly signals, routing, runbooks, rollback paths, customer-impact notes, and post-incident review templates.
Connect alerts to response
We train owners, document instrumentation patterns, leave runbooks, define review rituals, and hand over an improvement backlog.
Handover operating cadence

AI observability engagement models

Scoped options for teams moving AI systems from demo visibility to production reliability.

Diagnostic

AI Observability Gap Review

Best when debugging is slow or telemetry is incomplete

Scoped

after discovery

Workflow map

Trace schema review

Tooling recommendation

Instrumentation backlog

Talk to Sales
Most Popular

Implementation

Production AI Monitoring Setup

Best for LLM, RAG, or agent workflows entering production

Scoped

after discovery

Trace instrumentation

Eval pipeline

Dashboards and alerts

Incident playbooks

Talk to Sales

Ongoing

AI Reliability Operating Support

Best for teams with multiple AI products or agents

Scoped

after discovery

Quality reviews

Regression triage

Cost-quality reporting

Monitoring roadmap

Talk to Sales

Where AI observability helps most

This service is strongest when AI is no longer a prototype and product, support, operations, compliance, or leadership teams need reliable answers about behavior.

01

Customer-facing AI assistants

Monitor groundedness, answer quality, escalation, refusal behavior, response latency, user feedback, and source usage for copilots and chat experiences.

02

RAG and knowledge systems

Diagnose retrieval failures, stale documents, incorrect citations, chunking problems, reranking issues, missing-answer paths, and source-quality patterns.

03

Agentic workflows

Reconstruct why an agent selected a tool, what arguments it used, which approvals existed, what downstream system changed, and where a loop or retry began.

04

Document extraction and classification

Track extraction accuracy, schema errors, confidence thresholds, exception routes, human corrections, model changes, and document-type regressions.

Privacy, security, and telemetry control

AI observability can expose prompts, documents, user content, customer identifiers, and business context. We design telemetry so teams get diagnostic value without collecting more sensitive information than needed.

01

PII and sensitive-data handling

Define masking, redaction, hashing, sampling, allowlists, deny lists, retention rules, and review boundaries for prompt and output telemetry.

02

Deployment and hosting choices

Support hosted, self-hosted, private-cloud, warehouse-first, or hybrid observability patterns based on privacy, compliance, volume, and operational needs.

03

Access control and auditability

Separate who can view traces, prompts, production samples, customer data, evaluation labels, dashboards, and incident records.

04

Evidence without overcollection

Capture enough context to debug and govern AI systems while avoiding broad retention of raw documents, secrets, credentials, or unnecessary personal data.

Make production AI debuggable before the next incident

Share the AI workflows, incidents, logs, and tools you have today. We will help you identify which traces, evals, dashboards, and playbooks need to exist first.

Review our AI observability gaps View cost optimization service

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Trace design

Evaluation pipelines

RAG diagnostics

Incident playbooks

Frequently Asked Questions

Direct answers for teams comparing LLM observability, AI monitoring, evaluation pipelines, RAG diagnostics, and production agent tracing.

They include trace instrumentation, prompt and model version tracking, retrieval and tool-call monitoring, evaluation pipelines, dashboards, alerts, incident playbooks, cost telemetry, privacy controls, and team handover.

Traditional APM shows infrastructure health, request latency, and errors. AI observability adds semantic visibility into prompts, retrieved context, model decisions, tool calls, outputs, evaluations, user feedback, and quality regressions.

Yes. We can connect GenAI-specific telemetry to an existing observability stack when that is the right architecture, or use a dedicated LLM observability tool when traces and evals need a specialized workflow.

We can work with Langfuse, LangSmith, Arize Phoenix, Helicone, LiteLLM, OpenTelemetry-based pipelines, major APM tools, warehouse-first telemetry, or a custom observability layer depending on your needs.

Yes. Traces show what happened. Evaluations help determine whether the output was good, grounded, safe, complete, accurate, or regressed compared with a previous prompt, model, or release.

Yes. We can monitor retrieval relevance, source freshness, citation coverage, chunk quality, context size, reranking behavior, missing-answer patterns, and groundedness.

Yes. Agent monitoring often includes tool-call chains, loop depth, retries, memory use, permissions, approvals, downstream writes, failure causes, and incident reconstruction.

Yes. Cost telemetry can show spend by feature, workflow, model, user, tenant, prompt version, retrieval path, or agent step, then connect cost changes to quality and latency.

We define redaction, masking, hashing, sampling, retention, access control, deployment model, and data-export rules before broad telemetry collection begins.

Yes. We can help reconstruct incidents, identify missing telemetry, add trace coverage, create evaluation tests, define alerts, and write response playbooks to prevent recurrence.

Yes. Dashboards can cover quality, cost, latency, retrieval health, tool reliability, model errors, prompt versions, user feedback, production evals, and incident signals.

Useful inputs include AI workflow diagrams, current logs, prompts, model providers, RAG architecture, agent tool definitions, incident examples, data sensitivity requirements, and existing observability tools.

Yes. Traces, evals, model-version records, incident reviews, and dashboard evidence can support governance workflows, risk registers, model cards, and compliance discussions.

Handover can include instrumentation documentation, dashboard definitions, alert rules, runbooks, evaluation datasets, review cadence, privacy rules, and an improvement backlog.