NextEraLabs

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Workforce reference

Every agent role with its persona, job, collaborators and deliverable, plus lifecycle, guardrails and supported tools.

Canonical page: https://nexteralabs.com/

  • 18 agent roles
  • 4 layers: platform, data, ML, AI
  • 3 hyperscale clouds
  • 15+ years of data projects

What NextEraLabs offers

NextEraLabs provides an agentic workforce: 18 AI agent roles, each with a defined job, the tools for it and a deliverable a human team reviews. The roles are organised two ways: by platform layer (Platform, Data, ML, AI) and by lifecycle crew (Build, Develop, Run, Improve).

Layer: Platform

The cloud foundation everything else runs on.

Architect Agent (ARC)

  • Persona: The solution architect
  • Lifecycle crew: Build
  • What it does: Turns your requirements into a target architecture on AWS, Azure or Google Cloud.
  • Works with: Infrastructure Agent, Security Agent, Migration Agent, IT
  • Hands back: Reference architecture, decision records

Infrastructure Agent (INF)

  • Persona: The platform engineer
  • Lifecycle crew: Build
  • What it does: Provisions environments, networking and identity as code.
  • Works with: Architect Agent, Security Agent, Reliability Agent, FinOps Agent, IT
  • Hands back: Infrastructure-as-code pull requests, runbooks

Security Agent (SEC)

  • Persona: The security engineer
  • Lifecycle crew: Build
  • What it does: Sets up access, secrets and network policy, then checks every change against them.
  • Works with: Architect Agent, Infrastructure Agent, Governance Agent, Risk & Compliance
  • Hands back: Access model, policy findings

Reliability Agent (SRE)

  • Persona: The on-call engineer
  • Lifecycle crew: Run
  • What it does: Watches pipelines and platform, triages incidents, keeps the runbooks current.
  • Works with: Infrastructure Agent, Pipeline Agent, MLOps Agent, Quality Agent, IT
  • Hands back: Incident notes with root cause, fixes as pull requests

FinOps Agent (FIN)

  • Persona: The FinOps analyst
  • Lifecycle crew: Improve
  • What it does: Tracks cloud cost, finds the expensive workloads, proposes the fix.
  • Works with: Infrastructure Agent, Performance Agent, Finance
  • Hands back: Cost report, right-sizing pull requests

Layer: Data

Pipelines, models, quality and governance.

Migration Agent (MIG)

  • Persona: The migration lead
  • Lifecycle crew: Build
  • What it does: Reads your legacy warehouse — SQL, stored procedures, ETL jobs — and rebuilds it on the target platform.
  • Works with: Architect Agent, Pipeline Agent, Quality Agent
  • Hands back: Converted models, row-level reconciliation report

Pipeline Agent (PIP)

  • Persona: The data engineer
  • Lifecycle crew: Develop
  • What it does: Builds and maintains ingestion and transformation pipelines as reviewed pull requests.
  • Works with: Reliability Agent, Migration Agent, Quality Agent, Analytics Agent, Feature Agent, Performance Agent, Refactor Agent
  • Hands back: dbt models, orchestration DAGs, pull requests

Analytics Agent (ANA)

  • Persona: The analytics engineer
  • Lifecycle crew: Develop
  • What it does: Models the semantic layer, answers business questions and drafts dashboards.
  • Works with: Pipeline Agent, Governance Agent, AI Agent, Finance, Sales & Marketing, Operations
  • Hands back: Answers with the query shown, dashboards

Quality Agent (QA)

  • Persona: The data quality engineer
  • Lifecycle crew: Run
  • What it does: Writes the tests nobody has time for and watches freshness and anomalies.
  • Works with: Reliability Agent, Migration Agent, Pipeline Agent, Governance Agent
  • Hands back: Test suites, data quality findings

Governance Agent (GOV)

  • Persona: The data steward
  • Lifecycle crew: Run
  • What it does: Catalogs, classifies sensitive data, documents lineage, checks policy on every change.
  • Works with: Security Agent, Analytics Agent, Quality Agent, Knowledge Agent, Risk & Compliance
  • Hands back: Catalog entries, lineage, policy findings

Performance Agent (PRF)

  • Persona: The performance engineer
  • Lifecycle crew: Improve
  • What it does: Finds slow queries and jobs and tunes them.
  • Works with: Pipeline Agent, FinOps Agent, Refactor Agent
  • Hands back: Tuning pull requests with the query plans

Refactor Agent (RFX)

  • Persona: The maintainer
  • Lifecycle crew: Improve
  • What it does: Pays down technical debt: unused models, duplicated logic, overdue upgrades.
  • Works with: Pipeline Agent, Performance Agent
  • Hands back: Cleanup pull requests, deprecation plan

Layer: ML

Features, training and models in production.

Feature Agent (FEA)

  • Persona: The feature engineer
  • Lifecycle crew: Develop
  • What it does: Builds and maintains the feature store that models train and serve from.
  • Works with: Pipeline Agent, ML Agent
  • Hands back: Feature pipelines, feature documentation

ML Agent (MLE)

  • Persona: The ML engineer
  • Lifecycle crew: Develop
  • What it does: Builds training pipelines and runs experiments.
  • Works with: Feature Agent, MLOps Agent, Evaluation Agent, Operations
  • Hands back: Model candidates with an evaluation report

MLOps Agent (MLO)

  • Persona: The MLOps engineer
  • Lifecycle crew: Run
  • What it does: Deploys models, watches drift and triggers retraining.
  • Works with: Reliability Agent, ML Agent, Evaluation Agent
  • Hands back: Release records, drift reports

Layer: AI

Assistants and agents on your governed data.

Knowledge Agent (KNW)

  • Persona: The knowledge engineer
  • Lifecycle crew: Develop
  • What it does: Prepares documents, embeddings and retrieval indexes for AI applications.
  • Works with: Governance Agent, AI Agent, Product
  • Hands back: Retrieval indexes, source coverage report

AI Agent (AIE)

  • Persona: The AI engineer
  • Lifecycle crew: Develop
  • What it does: Builds assistants and agent workflows on your governed data.
  • Works with: Analytics Agent, Knowledge Agent, Evaluation Agent, Sales & Marketing, Product
  • Hands back: AI services with their evaluation suites

Evaluation Agent (EVL)

  • Persona: The evaluator
  • Lifecycle crew: Improve
  • What it does: Tests models and AI applications against evaluation sets and flags regressions.
  • Works with: ML Agent, MLOps Agent, AI Agent, Risk & Compliance
  • Hands back: Evaluation reports, regression findings

Lifecycle crews

  • Build — Stand the platform up. Roles: Architect Agent, Infrastructure Agent, Security Agent, Migration Agent.
  • Develop — Ship data, ML and AI products. Roles: Pipeline Agent, Analytics Agent, Feature Agent, ML Agent, Knowledge Agent, AI Agent.
  • Run — Keep it healthy every day. Roles: Reliability Agent, Quality Agent, Governance Agent, MLOps Agent.
  • Improve — Make it better every week. Roles: FinOps Agent, Performance Agent, Refactor Agent, Evaluation Agent.

Business lines

  • Finance — FinOps Agent, Analytics Agent
  • Sales & Marketing — Analytics Agent, AI Agent
  • Operations — ML Agent, Analytics Agent
  • Risk & Compliance — Governance Agent, Security Agent, Evaluation Agent
  • Product — AI Agent, Knowledge Agent
  • IT — Architect Agent, Infrastructure Agent, Reliability Agent

How it works

  1. Assess — We map your platform, your backlog and where agents can safely take work.
  2. Deploy — Agents get a role, tools and guardrails inside your own cloud tenant.
  3. Supervise — Your team approves. Every action is logged and reversible.
  4. Scale — Add roles as trust grows. Our engineers stay accountable for the outcome.

Guardrails

  • Runs in your environment — Your tenant, your data, your access model.
  • Human approval on every change — Agents propose through pull requests and tickets. People merge.
  • Everything is auditable — Each action, query and decision is logged.
  • Platform-native — Works with the tools you already run. No new lock-in.

Supported platforms

The workforce works natively on all three hyperscale clouds — single-cloud, multi-cloud or hybrid — and on the open-source stack.

  • AWS: S3, Glue, Redshift, Athena, EMR, Kinesis, Lake Formation, SageMaker, Bedrock, QuickSight
  • Microsoft Azure: Fabric, OneLake, Synapse, Data Factory, Stream Analytics, Data Activator, Power BI, Azure Machine Learning, Azure AI Foundry
  • Google Cloud: BigQuery, Dataflow, Dataproc, Pub/Sub, Dataplex, Data Fusion, Composer, Looker, Vertex AI
  • Open source (on any cloud or on-prem): dbt, Trino, Apache Iceberg, Apache Spark, Apache Flink, Apache Superset, Prefect, Apache NiFi, Keycloak, MLflow
  • Data solutions at any scale, anywhere, any tool — SaaS · PaaS · IaaS · On-prem

Company

NextEraLabs Information Technologies was established in 2020 in İstanbul. We are an expert team that has been implementing end-to-end data projects across many sectors for more than 15 years.

  • Industries: e-Commerce, Finance, Telco, FMCG, Manufacturing
  • Consultancy services: Cloud data transformation & migration; Data architecture & engineering; DWH modeling, re-engineering, re-factoring; Data lakehouse; Data governance & data quality; Business intelligence; Near-realtime warehousing; DataOps & MLOps; Customer analytics & forecasting; Fraud detection & computer vision

Contact

  • Email: info@nexteralabs.com
  • Address: Fenerbahçe Mah. İğrip Sk. No: 13/1, Kadıköy, 34726 İstanbul, Türkiye
  • Map: https://www.google.com/maps/search/?api=1&query=40.971571,29.0426549