Agentic AI at zeb

Ship useful agents, not just demos. Evaluate first, add guardrails, and run in production on your stack.

The Layers of Agentic AI

The Framework

The Model

The core and most flexible layer of the framework, responsible for routing tasks to the right model while balancing cost and value in a rapidly evolving model landscape.

Context

Defines how multi-variable signals are captured, filtered, and structured so agents receive relevant context while discarding noise.

Tools

Operationalizes agents by exposing deterministic actions through protocols like MCP and enabling agent-to-agent collaboration.

Human

Positions AI as a human accelerator rather than a replacement, with defined intervention points and approval checkpoints at high-value or high-risk stages.

Governance

Establishes deterministic guardrails that enforce platform, regulatory, and and brand policies by validating what enters and exits the agent system.

Observability

Provides end-to-end visibility by tracking every request, reasoning step, tool call, and agent delegation from input to response.

Evaluation

Measures whether agents deliver intended business value by defining success criteria and tracking accuracy and consistency over time.

KEY CONSIDERATIONS

The most fungible part of the framework, but also its core.

Rapidly changing environment, with new models releasing everyday.

Finding the equilibrium between cost & value.

Routing the task to the right model.

KEY CONSIDERATIONS

Apart from the model, how we interact with context is the most important part of the framework.

Extrapolating the multi-variable signals that we, as humans, have come to easily interpret into an Agent understanding way.

Managing the context, taking the good and leaving the bad.

KEY CONSIDERATIONS

Tools are how we make an agent an agent.

Providing limbs to something that was previously just a brain.

Extrapolating the logic in our heads as defined actions the agent can take.

MCP

The protocol that changed the world.
Any action a user can take is now an action that the agent can take through the protocol.

Agent to Agent

Agents themselves are inherently an action.
Agents can talk to other agents and can deconstruct what previously was a monolithic problem into a much smaller set of tasks, routed to an agent that is a subject-matter expert in that respective task domain.

KEY CONSIDERATIONS

At zeb, we believe AI is a human’s greatest accelerator but not its replacement.

Control & the ability to intervene is needed in any high value/high risk juncture.

Defining pre-determined approval points at these respective junctures is core.

KEY CONSIDERATIONS

Guardrails

Models on their own are generic, businesses are not.
Each business carries their own platform, regulatory, & brand policies.
What goes in & what comes out of our Agent need to be deterministically vetted.

Security

Security is not something that can be compromised for the sake of function.
Ways to keep authentication & federated requests from the organization to the individual user is extremely important.

KEY CONSIDERATIONS

Having the ability to see what’s happening under the hood is equally as important as driving the car.

From the time a request is sent until the response is delivered to the end user, every thinking step, tool call, or delegation to other agents must be tracked.

KEY CONSIDERATIONS

AI is meaningless if it isn’t delivering the business value it was sought out to originally achieve.

Defining the success criteria & reference of what good looks like.

Tracking accuracy as well as consistency.

Our Approach

A simple method that turns pilots into production. Evaluate. Govern. Ship in slices. Measure value.

Defining the Objective

Defining the problem.
Is AI the right solution?
- AI without value is meaningless.
- Starting with processes where human expertise already exists and can be codified.
- Ensuring we aren’t introducing non-determinism into a deterministic environment.
Reverse-engineering the solution
- Whether it is humans today or a different system taking care of the operation/problem being able to extrapolate the how from the steps they take today is pivotal in building any successful AI system.

Build

Putting it all together.
Translating the objective we defined & vertical moving parts into our horizonatal agentic framework.

Measure

Defining measurable points of success as well as clear indicators of failure.
Test the agentic system as an end user, not as its creator.

Iterate

Find out what worked & what didn't.
Follow the observability maps to understand what went wrong.
Tweak. Establish further context. Dumb it down. Repeat.

Platforms. Apply Our Approach On Your Stack

Choose your platform to see how zeb ships Agentic AI from first pilot to production with governance and measurement.

Ship A Production-Ready Agent On AWS In Weeks

Evaluation and guardrails are included from day one so your agent meets quality and cost targets.

01/ AWS Strands

A model-driven approach to agent development where the agent's behavior emerges from natural language instructions rather than rigid code paths.
Built-in reasoning loops that allow agents to dynamically plan, execute, and self-correct without predefined state machines.
Extensible tool architecture that treats everything, APIs, databases, other agents, as first-class capabilities the agent can discover and invoke contextually.

02/ AWS Bedrock AgentCore

Managed infrastructure layer that abstracts away the operational complexity of running agents at scale, session management, memory persistence, and multi-turn orchestration handled out of the box.
Native integration with AWS's identity and access framework, enabling agents to inherit fine-grained permissions and operate within existing security boundaries.
Built-in support for agent collaboration patterns, agents can spawn subagents, delegate tasks, and aggregate results without custom orchestration code.

Enterprise-grade agent execution on Databricks

Built-in orchestration, identity integration, and collaboration patterns ensure agents run securely and predictably at scale.

01/ Mosaic Agent

02/ MLflow

03/ AgentBricks

Deploy workflow-native agents faster

ServiceNow abstracts operational complexity so agents can plan, delegate, and complete work within trusted enterprise systems.

01/ Now Assist

Now Assist is the integrated generative AI suite, built into the Now Platform to maximize agent productivity and enhance self-service experience for employees and customers.
Migrate legacy NLU Virtual agent topics into LLM compatible conversational flows.
Now Assist panel for fulfillers to triage incoming cases/incidents with effective AI generated text.

02/ Now Assist Voice

Let users interact with the platform hands-free using natural language, enabling tasks like summarizing incidents, generating resolution notes, finding knowledge, and automating workflows via voice commands, significantly boosting efficiency for agents and field technicians on desktop or mobile.
ServiceNow remains the single pane of glass for agents, with CCaaS CTI embedded directly within the workspace.
Unified Voice calls routing through ServiceNow's Advanced Work Assignment engine, consolidating routing policies across all channels within ServiceNow.