Agentic AI implementation & operations

Run AI agents like a system, not a collection of demos.

We design, deploy and govern multi-agent, multi-model environments with Omnigent at the control layer, Databricks for enterprise AI and data, and MLflow for tracing, evaluation and operational visibility.

Design your agent platform ↗See the architecture ↓

Omnigent-centered implementation Databricks Consulting Partner MLflow observability & evaluation

Policy gate
Git push → ASK

Smart route
task → best model

live session

Customer-support automation
Omnigent / production workspace

€2.14 today

Planning agentClaude harness · high reasoning

ALLOW

Data agentDatabricks MCP · governed access

ASK

Action agentExternal tools · write operation

DENY

ClaudeGPT / CodexCursorPiCustom agentsDatabricks models

Core stack we implement around

DB Databricks

O Omnigent

ML MLflow

MCP MCP & enterprise tools

☁ AWS / Azure / cloud sandboxes

The gap

Agents are easy to prototype. Operating them is the hard part.

Once agents can choose models, call tools, touch files, reach company data and hand work to other agents, the problem stops being "which model?" and becomes architecture, permissions, observability, cost and operational control.

01 / FRAGMENTATION

Too many harnesses

Teams end up with separate Claude, Codex, Cursor and custom-agent workflows that are difficult to standardize or swap.

02 / GOVERNANCE

Prompts are not policy

Tool access, filesystem writes, network egress and spend need enforceable controls outside the model's own instructions.

03 / VISIBILITY

No operational picture

Without traces, evaluation and cost telemetry, it is hard to explain what an agent did, why it failed, or whether a new version improved.

04 / SCALE

Pilots do not equal platforms

Enterprise deployment introduces identity, data access, sandboxing, routing, deployment topology, auditability and team workflows.

The operating stack

One control plane. Your models, data and tools stay flexible.

We build an agent operations layer that keeps model and harness choices portable while putting governance, data access and observability around the full lifecycle.

Control layer

Omnigent

Use a common meta-harness to compose agents, switch models and harnesses, apply policies, isolate execution and supervise live sessions.

Multi-agent and multi-harness orchestration

Server, agent and session policy layers

Smart routing and model portability

Local, cloud and Kubernetes sandbox patterns

Enterprise AI & data

Databricks

Connect agents to governed enterprise models, APIs and data while retaining workspace-level identity and access controls.

AI Gateway / model access

Unity Catalog & governed data

Model Serving & MCP integrations

Observability & quality

MLflow

Trace complex agent execution, evaluate behavior against your criteria, compare versions and monitor production quality.

End-to-end traces

Evaluation datasets & scorers

Cost, latency and quality monitoring

Pragmatic by design.Omnigent is open source and currently alpha. We position it as an extensible control layer to evaluate and harden around your use case, not as a turnkey "install and forget" enterprise product.

Reference architecture

From intent to governed outcome.

A practical Omnigent-centered system can sit between users and AI execution, route work to the right model or agent, enforce policies before actions happen, and send telemetry into the observability layer.

Agentic operations architecture

Illustrative implementation pattern - adapt to the client's existing identity, data, cloud and tool stack.

● governed loop

01 / DemandPeople & teamsBrowser, mobile, IDE, chat, internal apps and shared live sessions.

02 / Product layerBusiness workflowsSupport, engineering, research, analytics, back-office automation.

03 / Control planeOmnigent orchestrationA common layer around agents and harnesses that can coordinate work, choose execution paths and apply controls before tools or systems are touched.

Multi-agent compositionModel / harness routingALLOW · ASK · DENY policiesSandbox & credential boundariesSession history & collaborationMCP / tool proxying

04 / IntelligenceModels & harnessesClaude, GPT/Codex, Cursor, Pi, custom agents, Databricks-served models and other gateways.

05 / ActionTools & systemsMCP servers, GitHub, databases, APIs, internal services, files and enterprise SaaS.

06 / ObserveMLflow tracing & evaluationTrace steps, inputs, outputs, latency, tokens and cost. Score quality in development and production.

07 / Governed dataDatabricks & cloud platformAI Gateway, Model Serving, Unity Catalog, governed enterprise data and cloud execution environments.

STEP 01DiscoverMap high-value workflows, systems, risk boundaries and success criteria.

STEP 02ComposeDefine agents, harnesses, models, tools and the handoffs between them.

STEP 03GovernAdd policy gates, sandboxing, credentials, budgets and access constraints.

STEP 04ObserveInstrument traces, evaluation, quality checks, cost and latency feedback.

STEP 05Operate & improveReview failures, tune routing and policies, then scale proven workflows.

What we implement

Agentic AI, built as an operational capability.

Atomicell bridges agent engineering, data platforms and product delivery so the work does not stop at a proof of concept.

Multi-agent orchestration

Design agent roles, delegation, handoffs and review loops across coding agents, custom agents and business workflows.

Multi-model architecture

Route tasks across model providers and harnesses based on capability, policy, cost and latency instead of hard-wiring a single vendor.

Policies & guardrails

Implement contextual ALLOW / ASK / DENY controls for tools, repositories, data access, budgets and risky actions.

Secure execution

Design local or cloud sandbox boundaries, network egress rules and credential patterns that reduce what an agent can reach by default.

Tracing & evaluation

Instrument MLflow or equivalent observability so teams can inspect behavior, compare versions and continuously evaluate quality.

Databricks integration

Connect agent workflows to governed enterprise models, Model Serving, data products and workspace-level controls.

Governance, not prompt theater

Put the rules outside the model.

Prompt instructions can guide behavior. Operational policy can decide whether an action is allowed, requires a person, or must be blocked.

agent.yamlpolicy example

name: customer_ops_agent

executor:
  harness: claude-sdk
  model: databricks-claude-sonnet

policies:
  github_access:
    type: function
    handler: ...github_policy
    factory_params:
      write_repos: ["org/support"]
      write_branches: ["feature/*"]

  session_budget:
    type: function
    handler: ...cost_budget
    factory_params:
      max_cost_usd: 8.00
      ask_thresholds_usd: [5.00]

runtime decisionspolicy engine

GET

Read approved support repositoryRepository is in the allowlist

ALLOW

Cross $5 session thresholdPause and ask before more expensive work

ASK

↯

Force push protected branchDestructive operation outside policy

DENY

NET

Call approved external APIMatches network egress rule

ALLOW

Policies can be layered at the server, agent and session level. We design the model around your organization's risk boundaries rather than relying on one global "safe mode".

Visual storytelling

Show the system, not generic AI stock art.

For the final site, use real product-style visuals: orchestration graphs, policy decisions, traces, cost charts and supervised sessions. They communicate capability better than abstract robots or glowing brains.

How we engage

Start focused. Prove control. Then scale.

We recommend a staged implementation so architecture, governance and evaluation mature together with the use case.

01 / ASSESS

Agent platform blueprint

Architecture, target workflows, tool/data map, governance requirements, model strategy and deployment options.

02 / PILOT

Governed working system

One real workflow with Omnigent orchestration, model routing, policies, sandboxing and initial traces.

03 / HARDEN

Operational readiness

Identity, secrets, network boundaries, evaluation, failure modes, cost controls, observability and deployment automation.

04 / SCALE

Reusable AI operations layer

Expand to more agents and teams with shared patterns, policy libraries, dashboards and continuous quality loops.

Databricks

Atomicell - Official Databricks Partner

Atomicell is a registered Databricks Consulting Partner in the EMEA region. We help businesses unlock the power of their data by building scalable infrastructures, automating data pipelines, and integrating AI solutions to accelerate digital transformation.

Let's Connect! ↗Our Expertise ↓

Databricks AWS Azure

registered Databricks Consulting Partner

EMEA region

01 Designing and building data lakehouses

02 Data pipeline automation and orchestration

03 Cloud platform deployment and support (AWS, Azure)

04 End-to-end web application development

05 Consulting and system integration on Databricks-powered platforms

What do we offer?

Innovative Solutions, Tailored for Growth

From rapidly launching MVPs to managing data at scale and crafting seamless user experiences, we provide comprehensive solutions to drive your product’s success. Whether it’s validating your ideas, optimizing data performance, or enhancing user engagement, our team brings the expertise to elevate every phase of your product’s journey.

01 / MVP

Fast Launch

We help you quickly develop and deploy MVPs based on your technical requirements, allowing you to test your ideas in the market.

Rapid development and deployment of MVPs tailored to client specifications.
Streamlined processes to validate concepts and gather user feedback.
Scalable architecture design to accommodate future growth.

02 / DATA MANAGEMENT

Effective scaling

We provide effective data management and application scaling to handle large volumes of data and high loads.

Data collection and ingestion from various sources.
Efficient data storage solutions and maintenance.
Automation of data processing pipelines for seamless operations.
Continuous monitoring and security compliance.

03 / UI/UX

Smart design only

We create intuitive interfaces and optimize user interaction with your products.

Usability analysis to identify interface issues.
Prototype creation for testing ideas through wireframes and interactive models.
Development and maintenance of design systems for consistency.
Responsive design optimization for all types of devices.

Our Specialties

Turning Your Vision into a Clear Plan

We help refine your initial idea into a well-defined roadmap, focusing on business goals and user needs. With our expertise, you’ll have a detailed plan that minimizes risks and maximizes potential.

Building a Functional and Scalable MVP

Our team rapidly develops a robust MVP tailored to your specifications, ensuring scalability and performance. We prioritize efficient development without compromising quality, setting the foundation for long-term success.

Ensuring Stability and Seamless Operations

We provide ongoing monitoring, updates, and support to ensure your application runs smoothly. Our proactive approach minimizes downtime and keeps your product secure and up to date.

Scaling Your Product to Meet Demand

As your business grows, we optimize and expand your application to handle increased user demand. With expertise in data scaling and new feature development, we ensure your product evolves with your audience.

Our Expertise

Technologies We Use

Our team has extensive experience with a wide range of technologies, allowing us to craft innovative solutions tailored to your needs. From frontend development to backend infrastructure, we leverage the latest tools and frameworks to deliver exceptional results.

By Technologies