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Agentic AI Workflow Design & Implementation on AWS

Cloud Combinator designs and implements agentic AI systems on Amazon Bedrock, turning AI experiments into robust, observable workflows that integrate cleanly with your existing applications and data.

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Who it's for

This service is perfect for:

  • SaaS and ISV teams adding agentic AI features into their products.
  • FinTech and other regulated platforms exposing financial or customer data via AI.
  • Startups moving from LLM prototypes to a standardised, production workflow on AWS.
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What This Service Does...

We help teams move from ad hoc LLM usage to structured agentic workflows on AWS. The service covers architecture, implementation and handover for systems where an agent coordinates multiple tools (for example: data queries, rule management and validations) using Amazon Bedrock and a serverless tooling layer.

Example use cases

Where this agentic pattern is useful

A few concrete ways teams are using agentic workflows on AWS.

Financial insights & reporting

  • Ask questions about spend, revenue or risk in natural language.
  • Return validated queries and structured explanations, not free-form text.

SaaS product copilots

  • Guide users to create rules, filters and configurations through chat.
  • Let tools perform the underlying CRUD operations safely in the background.

Customer support workflows

  • Classify and summarise incoming tickets or messages.
  • Fetch relevant account context and propose bounded next actions.

Operations & data quality

  • Run routine checks over new data loads or events.
  • Open issues or tasks automatically when validations fail.

Core Components of the service

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Architecture & design

Target architecture for your agentic workflows on AWS, including agents, tools, data flows, access boundaries and observability.

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Agent & prompt design

Design of Amazon Bedrock agents, prompts and action groups so user intent is interpreted consistently and routed to the right tools.

3-4

Tooling & integrations

Lambda-based tools that perform deterministic actions such as queries, CRUD operations, validations and classifications against your systems.

1-4

Data access & security

Clear patterns for how the agent accesses data sources and APIs, enforced with least-privilege IAM and tenant- or token-scoped permissions.

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Observability & logging

End-to-end logging and metrics for prompts, tool calls, errors and user request IDs, with dashboards for health and performance.

7

Handover & extensibility

Infrastructure as code, runbooks and training so your teams can operate the system and add new tools and workflows without re-architecting.

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WHY Amazon Web Services?

How the service fits into your existing controls

This section explains how the agentic workflow is hosted on AWS, how access is controlled, how activity is monitored, audited, and why it matters when you run AI systems in production

Region & data residency +

Deployments can use EU-region Amazon Bedrock models and surrounding AWS services so data residency and locality requirements are easier to satisfy, and AI workloads stay aligned with your existing regional strategy.

Access boundaries & permissions +

Any built agent only reaches systems exposed through its tooling layer, enforced with least-privilege IAM, network controls and token- or tenant-scoped access so data and operations remain within clear, reviewable boundaries.

Audit, logging & observability +

Prompts, tool calls, decisions and responses are logged with request-level identifiers into your existing observability stack, making behaviour traceable, debuggable and suitable for internal or external review.

Change management & migration +

Agents, tools and supporting infrastructure are defined as code, so updates flow through your normal CI/CD pipelines; where needed, we also design a clear path from existing LLM usage towards Amazon Bedrock.

Typical Challenges Companies Face with Agentic AI Systems

Teams experimenting with LLMs often run into similar issues when they try to move into production:

  • Scattered prototypes: multiple teams calling LLMs directly with no shared pattern or governance.

  • Unclear data boundaries: uncertainty over which systems the agent can access and how that is enforced. 

  • Limited observability: it’s hard to explain why an agent took a particular action or to trace full request flows. 

  • Difficult to extend: adding new tools or workflows can require reworking large parts of the system.

This service provides a structured approach to define architecture, controls and tooling so agentic systems can be operated and evolved with confidence.

48 %

Of AI projects ever reach production

Gartner survey 2025

96 %

of our projects successfully delivered

Cloud Combinator 2025

How the Agentic AI Workflow Service Works

01

Discover & design

We map your current workflows, data sources and constraints, then define a target architecture for an agentic workflow on AWS, including agents, tools, data boundaries and observability.

Who is involved

  • Cloud Combinator architect / AI specialist
  • Your product and engineering stakeholders

Time commitment

Typically 1–2 workshops

02

Implement on Amazon Bedrock

We configure the Amazon Bedrock agent, implement the Lambda-based tools, set up guardrails and IAM, and wire the workflow into your applications and data sources using infrastructure as code.

Who is involved

  • Cloud Combinator engineering team
  • Your engineering / platform team

Time commitment

Varies by scope; often a few weeks

03

Pilot & harden

We run a pilot with real users and data, adjust prompts and tool contracts, and test behaviour, performance and failure modes under realistic conditions.

Who is involved

  • Cloud Combinator architect / engineer
  • Your product, engineering and operations stakeholders

Time commitment

Typically 1–2 weeks of pilot activity

04

Production & handover

We promote the workflow to production, finalise dashboards and runbooks, provide knowledge transfer and support an agreed hyper-care period while your team takes over day-to-day operation.

Frequently Asked Questions

What is the Agentic AI Workflow Design & Implementation service?

It’s a consulting and build engagement where we design, implement and hand over a production-ready agentic workflow on AWS, using Amazon Bedrock agents, Lambda-based tools and your existing applications and data sources.

What kinds of problems is this suitable for?

Typical examples include financial or operational insights, SaaS product copilots, internal operations automation and controlled access to data or actions via natural language.

Do you only work with Amazon Bedrock?

This service is centred on Amazon Bedrock agents and models, but we can incorporate existing usage of other providers where needed and design a clear migration or coexistence path.

What does my team need to provide?

You bring knowledge of your domain, data and systems, access to the relevant AWS accounts and stakeholders from product, engineering and security; we bring the architecture, patterns and implementation experience.

How long does a typical engagement take?

Timelines depend on scope, but many projects follow the four phases described above, with design, implementation, pilot and handover delivered as a single structured engagement.

How do you handle security and data privacy?

We use AWS-native controls such as IAM, VPC endpoints and encryption, define clear data access boundaries and implement guardrails so agents can only perform bounded, auditable actions.

Can we extend the system after the engagement?

Yes. The architecture is designed around action groups and tools, so you can add new capabilities over time without redesigning the core workflow.

 

Other Real-world examples of our AI solutions

Our case studies show how organisations are using AI and AWS to solve concrete problems. Each engagement combines AI techniques with AWS-native services to deliver systems that can be operated, monitored and iterated on in production.

GET IN TOUCH

Book an agentic AI design session

Whether you are planning or already building agent-based AI features on AWS, meet with a senior consultant to explore where agents can add value, how they should interact with your systems and what a workable architecture on Amazon Bedrock looks like for your organisation.

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