Agentic AI Engineering

AI agents that run inside your operations, not in a demo

We design, build and operate LLM agents, multi-agent systems and computer-vision models that act on live data in the systems you already run. Guardrails, evaluation and observability are part of the build, not an add-on.

Agentic AI Engineering is the Erbis service for building AI agents, LLM orchestration, multi-agent systems and computer-vision models that run in production inside ERP, WMS, TMS, EHR and CLM systems. It is for operations and technology leaders in supply chain, healthcare, manufacturing and legal who need AI that acts on live data with governance, evaluation and observability built in.

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AI accuracy in production, invoice automation across 10+ companies
~95%
first production AI system shipped
2017
saved with AI invoice processing at ~95% straight-through
$10M+/yr
to a first working agent on your data
2 weeks
The problem

Why most agent projects stall between the demo and the P&L

MIT's NANDA initiative found that 95% of enterprise GenAI pilots show no measurable P&L return, and Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. The model is rarely the problem. The demo works. What fails is everything around it: the data the agent reads, the systems it writes to, and the rules that decide when a person must step in.

Agents raise the bar. A chatbot that answers wrongly is embarrassing. An agent that books the wrong carrier, resets the wrong password or approves the wrong invoice is expensive. Deloitte's 2026 State of AI survey of 3,235 leaders found only 1 in 5 organizations has a mature governance model for autonomous agents, while Gartner expects 40% of enterprise applications to embed task-specific agents by the end of 2026, up from under 5% in 2025. Procurement teams now ask for documented data handling, model governance and audit-log access before they sign.

Erbis has shipped production AI since 2017 and has built the systems agents plug into for longer: TMS, WMS, ERP and identity platforms, with 4,000+ integrations completed. We build agents the way we build software: with tests, evaluation sets, cost limits, tracing and a defined path to the person who owns the outcome. MIT's same study found external partnerships succeed at twice the rate of internal builds, 67% against 33%. We do not sell an agent platform. We build on yours.

What we do

Capabilities

AI agents for autonomous operations

Agents that read from and write to the systems your teams already use, with a human checkpoint where judgment matters.

  • Order tracking and shipment status agents that update the TMS without a person in the loop
  • Identity and access agents: account setup cut from 15 min to under 1 min per request
  • Invoice and document agents with confidence gates and human-in-the-loop review
  • Scheduling and cross-system data entry across CRM, ERP and WMS
  • Defined escalation paths, so every agent knows when to stop and ask

LLM orchestration and multi-agent systems

Stateful, multi-step workflows built with LangGraph and LangChain, on a model layer you can swap.

  • Multi-model strategy: Claude, OpenAI and Gemini behind one abstraction layer
  • Stateful multi-agent orchestration in LangGraph, with retries, timeouts and budgets
  • Tool use against your APIs, with schema validation on every call
  • RAG pipelines over contracts, manuals, tickets and operational records
  • Semantic Kernel and Bedrock Agents where the client stack is Microsoft or AWS-native

Document intelligence and generative AI for content

Extraction, summarization and generation over the documents that carry your operation: invoices, contracts, bills of lading, clinical notes.

  • Invoice extraction at ~95% straight-through processing, 15x less manual work
  • Contract generation with a custom-trained LLM: 10x faster, €3.5M/yr saved for a law firm
  • Summaries and reports for operations, finance and customer support
  • Verification controllers that check LLM output against deterministic rules before release

Computer vision and ML in production

Recognition and prediction models that run against live streams and real volumes, retrained on a schedule.

  • Image, video and text recognition for compliance, quality control and automation
  • Predictive models such as shipment ETA: RMSE 21.4 h across 304K shipments on SageMaker
  • Forecasting and anomaly detection on operational data
  • Retraining pipelines with drift monitoring and evaluation gates

Managed AI services for rapid integration

When a managed model is enough, we use it. We do not build a custom model for a problem Bedrock, Azure OpenAI or Vertex AI already solves.

  • Bedrock, Azure OpenAI and Vertex AI wired into existing workflows
  • AI-assisted customer support: chat, call routing and FAQ automation
  • Fraud and anomaly detection on transactions
  • Personalization and recommendation engines

Agentic engineering for your software team

We convert existing engineering organizations to agentic development: context engineering, verification pipelines and team-wide standards, at a predictable cost.

  • Codebase context audit: semantic dependencies, documentation gaps, machine-readable conventions
  • Verification pipelines (tests, static analysis, agent self-review) that cut the review tax on senior engineers
  • Shared, versioned agent workflows instead of one-off IDE setups
  • Delivery metrics re-baselined: DORA plus agent task success, verification time and rework

Audit, support and optimization

Ongoing evaluation, fine-tuning and cost control for AI systems already in production, ours or someone else’s.

  • AI audits: accuracy, bias, cost and compliance against a written baseline
  • Prompt and model upgrades with regression evals before rollout
  • Caching and routing that cut LLM API spend: −90% on one identity-management system
  • Multi-cloud performance and cost tuning
In production

How it runs in production

Every agent we ship has four things in place before go-live. Procurement now asks for documented data handling, model governance and audit-log access as contract conditions; we build to that bar by default.

01

Data governance

Agents get least-privilege access to named systems and fields. PII and PHI are masked before they reach a model, or the model runs inside your VPC. Every tool call and model call is logged with input, output and the identity that triggered it. Deployment on AWS, Azure, Databricks or on-premises, in your account, under your keys.

02

Evaluation and testing

A written eval set before the first prompt: golden cases, edge cases and failure cases drawn from your own records. Every prompt, model or tool change runs against it. Agents ship with unit tests for tools, regression evals for behaviour and a confidence gate that routes low-certainty cases to a person.

03

Observability and cost

Tracing on every run: steps, tool calls, tokens, latency and outcome. Budgets per agent and per task, with hard stops. Caching and model routing keep cost per task flat as volume grows. Dashboards live in your monitoring stack, not ours.

04

Integration and handover

Agents connect through your ERP, WMS, TMS, EHR or CLM APIs, not around them. You own the code, prompts, evals and infrastructure from day one. Handover includes runbooks, on-call procedures and a training week for your engineers. Exit is available at every phase.

Proof

What we have built

All case studies

How it works

How we work with you

  1. 01

    Discovery

    One to two weeks. We map the workflow, the systems the agent touches, the data it needs and the decision points where a person must stay. Output: a scoped use case with a success metric in your operating terms, an architecture sketch and a fixed-fee pilot proposal.

  2. 02

    Pilot

    Four weeks, fixed fee. A production-grade agent on a bounded scope, running on your data with evals, tracing and a confidence gate. Something to show in week 2. Ends with a go/no-go decision and a production plan.

  3. 03

    Production

    Hardening, integration and rollout. Guardrails, access controls, cost limits and runbooks. Two-week sprints with a demo at the end of each. Your engineers pair with ours so the system is understood before it is handed over.

  4. 04

    Run and scale

    We operate the agent with you or hand it over. Ongoing evals, model upgrades and cost tuning under an SLA. New use cases reuse the same platform. 89% of clients stay with us longer than 12 months.

Stack

Technologies we build with

Models

  • Anthropic Claude
  • OpenAI
  • Google Gemini
  • Llama
  • Amazon Bedrock
  • Azure OpenAI
  • Google Vertex AI

Orchestration and agents

  • LangGraph
  • LangChain
  • Semantic Kernel
  • Bedrock Agents
  • RAG pipelines
  • Model Context Protocol (MCP)

ML and vision

  • Amazon SageMaker
  • Amazon Textract
  • SageMaker Ground Truth
  • XGBoost
  • Python

Runtime and security

  • AWS Lambda
  • Kubernetes
  • Terraform
  • DynamoDB
  • Amazon S3
  • AWS KMS
  • OAuth 2.0
FAQ

Agentic AI Engineering: questions we get asked

What is agentic AI, and how can autonomous AI agents support my team?

An AI agent is software that uses a language model to plan and execute multi-step tasks against real systems: reading a shipment record, calling a carrier API, updating the TMS, escalating to a person when confidence is low. It differs from a chatbot in that it acts rather than answers. In production we have used agents for account provisioning, invoice processing, shipment status updates and contract drafting. Your team keeps the decisions that need judgment; the agent carries the routine volume.

Do I need custom machine learning models, or can you integrate existing AI capabilities?

Usually you do not need a custom model. Most agent work uses managed models (Claude, OpenAI or Gemini through Bedrock, Azure OpenAI or Vertex AI) with your data supplied through retrieval and tools. We train or fine-tune when the task is narrow and the data is yours, such as shipment-ETA prediction on 304K shipments or a contract-specific LLM for a law firm. We tell you which applies in discovery, before you spend on training.

Can agentic AI integrate with my existing systems and data sources?

Yes. That is most of the work. Agents connect through the APIs of your ERP, WMS, TMS, EHR, CLM or identity provider; where an API does not exist, we build one or use EDI, file drops or database replication. Erbis has completed 4,000+ integrations, including 60+ carrier APIs for a global TMS. Legacy systems do not block an agent project. Undocumented ones slow it down, which is why we audit integration points in week 1.

Is my data secure when using your AI solutions?

Your data stays in your cloud account or on-premises, under your keys. Agents run with least-privilege credentials scoped to named systems and fields. PII and PHI are masked before they reach a model, or the model runs inside your VPC. Every model call and tool call is logged for audit. Erbis is ISO 27001 certified, signs DPAs and BAAs where required, and every engineer works under an NDA and an IP-assignment agreement.

How quickly can I expect results from an agentic AI project?

You see a working agent on your data in 2 weeks and a production-grade pilot with a go/no-go decision in 4 weeks. Production rollout typically takes 2 to 4 months depending on integrations and approvals; the contract-automation system for a law firm was in production in 4.5 months. Measured business results follow rollout, not the demo, which is why we define the metric in week 1.

Do you provide ongoing AI audits, optimization and support?

Yes. After go-live we run the agent with you or hand it over with runbooks and training. Ongoing work includes regression evals for every model or prompt change, drift monitoring, cost tuning (caching cut one client’s LLM API spend by 90%) and periodic audits of accuracy, bias and compliance. We also audit and optimize AI systems built by other vendors or by internal teams.

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Bring us a workflow, not a use-case list

Tell us the process, the systems it touches and the number you want to move. Within a week you get a scoped pilot, a fixed price and the metric we will be judged on.

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