---
title: "Agentic AI Engineering"
url: https://monogram.io/engineering
description: "We design and deploy custom agentic AI systems that connect to your products, data, and operations to automate entire workflows end to end. No generic software. No massive migrations."
---

# Engineering

## Agentic AI engineered for production

We design and deploy custom agentic AI systems that connect to your products, data, and operations to automate entire workflows end to end. No generic software. No massive migrations.

---

## Production proof

- **< 2 sec** — Voice response on live customer calls
- **92%** — automation on a live customer facing workflow
- **800–1,000** — daily users served by a 9 agent pipeline in production
- **73%** — token reduction through architectural optimization, no quality loss


## Clients

- [Vercel](/technology/vercel.md) *(Client + Partner)*
- [Google](/work/real-time-collaboration-in-a-cloud-ide.md) *(Client)*
- [GitHub](/technology/github.md) *(Client)*
- [IBM](/work/ai-alliance.md) *(Client)*
- [United Healthcare](/technology/united-healthcare.md) *(Client)*
- [Stanford](https://ftl.stanford.edu) *(Client)*
- Zenity
- Contextual AI
- Abnormal


## A few shapes of agentic systems we ship

### 01. Multi-agent orchestration

Specialized agents with single responsibilities, dynamic instruction injection, programmatic validators where determinism matters. Production proof: a 9-agent pipeline serving 800-1,000 daily users.

### 02. Voice & conversational interfaces

Built on Amazon Connect, Lex, or GCP equivalents. Sub-2 second latency. Real customer transactions, not demos. Live API integration with payments, inventory, and identity.

### 03. MCP & RAG knowledge agents

Agents grounded in your real data through MCP-connected knowledge bases, vector stores, and live API access. No hallucination from training data. Full audit trail of what context shaped each answer.

### 04. Internal copilots & workflow automation

Multi-step reasoning, human-in-the-loop, memory across sessions. For due diligence, document processing, customer service, reconciliation, sales enablement, and the long tail of internal work that consumes expert capacity.

## Agent ops and evaluation systems

<mark>Most agents pass the demo and drift in production. This is the layer that keeps them honest.</mark>

Review queues, eval suites, telemetry, and regression tests that make agent behavior measurable instead of anecdotal. Low confidence outputs route to people, drift gets caught before users feel it, and prompts, tools, and policies improve against real usage rather than a static test set.

### 01. Observability & telemetry

Every run is logged: what the agent was asked, which tools it called, what it decided, what it returned. When something breaks, the cause traces to the exact step that produced it rather than a hunt through the whole chain.

### 02. Evaluation

The agent runs against a suite of test cases that score quality, backed by regression tests so a change today doesn't quietly break behavior that worked yesterday.

### 03. Drift detection

Behavior that degrades over time gets caught early, whether the cause is a model update, shifting real world inputs, or a prompt edit from last week.

### 04. Human in the loop

Low confidence or high risk outputs go to a person instead of the agent acting on its own. The agent escalates what it shouldn't decide alone.

### 05. Feedback loops

Real production data drives the next round of prompt, tool, and policy improvements, not guesswork.

**REGRESSION SUITE ON EVERY RELEASE · ~8% ROUTED TO REVIEW**

## Applied AI Showcase

### AI deployments across multiple clouds

- [Multi-agent AI solution generator for a developer platform at scale](/work/multi-agent-ai-solution-generator-for-a-developer-platform.md) — AI Agents
- [Conversational AI booking agent for inbound voice via Amazon Connect](/work/conversational-ai-booking-agent.md) — AI Agents

## Why Monogram

Applied AI sits on top of years of production engineering, not the other way around.

### 01. Engineering depth, not slideware

Our public case studies show architecture, model selection rationale, and production tradeoffs. That is the work, not the wrapper.

### 02. Composable foundations since 2017

We've shipped enterprise software for Google, Delta, IBM, Vercel, GitHub, and dozens of teams. Applied AI builds on years of production engineering, not the reverse.

### 03. Cloud and model neutrality

We're not selling you our preferred stack. We're picking the right cloud, model, and framework for your workload, your existing infrastructure, and your data sovereignty requirements.

### 04. MCP-native and protocol forward

We've been building on the composable thesis since before MCP existed. Now that it does, our agents work from real data through standardized protocols, not custom integrations that break on the next release.

## Other Engineering Services

- [Composable Architecture](/composable-architecture.md)
- [eCommerce](/engineering/ecommerce.md)
- [Frontend](/frontend.md)
- [Backend](/backend.md)
- [Data & APIs](/data-and-apis.md)

## Contact Us

[Build what comes next](https://monogram.io/#contact) | Email: [hello+llm@monogram.io](mailto:hello+llm@monogram.io)

Provide your contact details and claim a time to meet. We'll help you do the rest.

Monogram is an Applied AI Studio based in Atlanta that designs and deploys production AI systems — connecting models, data, workflows, and enterprise software to automate real work.

The contact form collects: name, email, company, estimated budget (USD), a project description, and how you heard about Monogram.

Not sure where to start? Tell us what you're trying to automate or build — we'll help you figure out where AI fits.


---

**More content:** [Home](/.md) | [About](/about.md) | [Blog](/blog.md) | [Work](/work.md)

Monogram helps companies, organizations, and firms adopt AI by designing and building proof of concepts (POCs), MVPs, intelligent workflows, and production-ready AI applications.

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