Agent infrastructure on AWS

Get your AI agents out of the notebook and into production with Amazon Bedrock AgentCore

The prototype was great in the demo. Then someone asked how it remembers customers, whose permissions it uses when it calls your CRM, and where the audit trail lives. AgentCore answers those questions. We design it into your AWS estate and help you run it.

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Fig. 01 Agent reference architecture
RT

Runtime

Serverless hosting, one isolated session per user

MEM

Memory

Short-term and long-term recall across sessions

GW

Gateway

Your APIs and Lambda functions as MCP tools

Your agent Any framework, any model
  • Strands Agents
  • LangGraph
  • CrewAI
  • LlamaIndex
  • Custom code
Amazon Bedrock models or another provider
ID

Identity

Acts on behalf of the signed-in user

OBS

Observability

Step-by-step traces in CloudWatch, OTel compatible

TOOLS

Tools

Sandboxed Code Interpreter and cloud Browser

Amazon Bedrock AgentCore logo AWS logo
Project Amazon Bedrock AgentCore
Drawn by ConsultEdge.Global
500+
Customers worldwide
50+
Technology alliances
24*7
Global delivery centre
IN + AE
Offices in India and Dubai

Talk to an architect

Got an agent that works in the demo?

Tell us what it does today and where it needs to run. An AWS architect from our team, not a bot, will get back to you within one business day.

02 Definition

What Amazon Bedrock AgentCore actually is

AgentCore is a set of managed AWS services for running AI agents in production. It is not a new framework and it does not replace the one you picked. It is the plumbing around your agent: where it runs, what it remembers, which tools it can reach, who it is acting for, and what it did.

You can build that plumbing yourself. Most teams who try spend months on session handling, credential brokering and tracing before the agent does anything new. AgentCore gives you those pieces as services, and you only pay for what each one uses.

One honest caveat. AgentCore is infrastructure for developers. If you want to set up an agent through configuration inside Bedrock, Agents for Amazon Bedrock may be a better fit, and we will tell you so.

Spec sheetAgentCore
Type
Modular, fully managed AWS services
Frameworks
Strands Agents, LangGraph, CrewAI, LlamaIndex or your own code
Models
Amazon Bedrock or other providers
Adoption
Use one service, a few, or all of them
Pricing
Consumption based, no upfront commitment
Regions
Multiple AWS Regions, including Asia Pacific

03 Failure analysis

Prototype vs production

Every agent we have taken live hit some version of these. The left column is fine for a demo. The middle column is what your users, your security team and your auditors will ask for.

In the notebook What breaks in production AgentCore piece
01Runs on a laptop or in one container. Lots of users at once, each needing their own isolated session. Some tasks run for hours. Runtime
02Forgets everything when the kernel restarts. Customers repeat themselves every time. The agent cannot recall a preference it was told last week. Memory
03Calls APIs with a key pasted into a .env file. Internal APIs have to be exposed as tools safely, without every team writing its own glue code. Gateway
04Acts as the developer who wrote it. It must act as the real user, with that user's permissions, through Cognito, Okta or Microsoft Entra ID. Identity
05Does whatever the prompt lets it do. Rules like "no refunds above the approved limit" must hold every time, enforced outside the model. Policy
06You debug with print statements. Someone asks why the agent did that on Tuesday. You need the full trace, step by step. Observability
07"Looks right" after a few test chats. Quality drifts as prompts, models and data change. You need scores from real traffic, not gut feel. Evaluations
08Runs generated code on your own machine. Code execution and web browsing need a sandbox that cannot touch anything else. Code Interpreter & Browser

04 Components

The stack, layer by layer

Read it bottom up. Runtime is the floor everything stands on. Your agent sits at the top, and it is still yours: your framework, your prompts, your model choice.

L5 Your agent You own this layer

Agent logic

Prompts, tool definitions and orchestration in the framework your team already knows.

Model

Foundation models from Amazon Bedrock or another provider.

L4 Tools What the agent can do

Gateway

Turns existing APIs, Lambda functions and services into MCP-compatible tools agents can discover.

Code Interpreter

A sandbox where the agent can write and run code for calculations and analysis.

Browser

A managed, isolated cloud browser for web-based tasks.

L3 Context What the agent knows

Memory

Short-term memory for the current conversation, long-term memory for facts and preferences across sessions.

L2 Trust What the agent is allowed to do

Identity

Agent and user credentials, integrated with Amazon Cognito, Okta or Microsoft Entra ID.

Policy

Deterministic rules on tools and data, enforced outside the agent code.

L1 Execution Where the agent runs

Runtime

Serverless hosting with session isolation, fast cold starts and support for multi-hour workloads.

Architect's note. You do not need every layer on day one. Plenty of teams start with Runtime and Observability, then add Memory or Gateway when a real need shows up. Each service also works on its own, so agents hosted elsewhere can still use Gateway and Identity.

05 Engagements

How we can help, start to finish

Five engagements that line up as a roadmap. Most clients join at one point on it, not at the start. Timelines are typical ranges; we confirm yours after a first call.

  1. Agentic AI strategy and use case discovery

    We work out where an agent will save real time or money, and where a simpler automation would do. Then we check whether your data and systems are ready for it.

    • Use case discovery and prioritisation
    • How much autonomy, and where a human signs off
    • Framework and model selection
    • Business case and success metrics
    Output
    A prioritised agent roadmap and a reference architecture on AgentCore.
    Typical duration
    2 to 3 weeks
  2. Custom agent development

    Our engineers build single-agent and multi-agent systems in Strands Agents, LangGraph or CrewAI, on Amazon Bedrock models, deployed to AgentCore Runtime.

    • Agent design, prompts and tool definitions
    • Multi-agent orchestration
    • Memory design for personalisation
    • RAG with Amazon Bedrock Knowledge Bases
    Output
    A working, tested agent running in your AWS account.
    Typical duration
    4 to 8 weeks
  3. Prototype to production migration

    Already have agents in notebooks, containers or on self-managed servers? We harden them and move them to AgentCore, without making you rewrite the agent logic.

    • Agent and framework assessment
    • Containerisation and Runtime deployment
    • Gateway setup for your existing APIs
    • Load, security and failure testing
    Output
    Production agents with CI/CD, monitoring and runbooks.
    Typical duration
    3 to 6 weeks
  4. Agent security, identity and policy

    Our CERT-In empanelled security practice designs the identity, access and policy controls, so each agent can do its job and nothing more.

    • Identity provider and OAuth integration
    • Policy definitions for tools and data
    • Amazon Bedrock Guardrails for content safety
    • Threat modelling and agent VAPT
    Output
    A documented security architecture and compliance evidence.
    Typical duration
    2 to 4 weeks
  5. Managed AgentOps, 24*7

    Once agents are live, our Global Delivery Centre watches them around the clock, checks their quality and keeps token spend in line.

    • Observability dashboards and alerting
    • Continuous quality evaluations
    • Cost and token usage tuning
    • Incident response and model updates
    Output
    A monthly AgentOps report on quality, reliability and cost.
    Typical duration
    Ongoing

06 Method

How we work

The same five steps on every agent, big or small. Security is a step of its own, not something we bolt on at the end.

  1. 01

    Discover

    Agree the use case, how much autonomy it gets and how we will measure success.

  2. 02

    Architect

    Pick the framework, the model and only the AgentCore services this agent needs.

  3. 03

    Build

    Write the agent, its tools, its memory and its Gateway integrations.

  4. 04

    Secure

    Identity, policy, guardrails, then we try to break it.

  5. 05

    Operate

    Watch it, score it and improve it with real traffic.

Why teams bring us in for this

Agents touch your data, your systems and your customers, so the people building them need to understand security as well as AI. We hold the AWS Agentic AI and Generative AI competencies and run a CERT-In empanelled security practice, which means the same team can build the agent and sign off on how it is locked down.

Talk to an architect
  • AWS Advanced Tier Partner
  • AWS Agentic AI & Generative AI competencies
  • CERT-In empanelled
  • ISO 9001:2015 & ISO 27001:2022
  • 24*7 Global Delivery Centre
  • Offices in India and Dubai

07 Applications

Where agents earn their keep

Agents that take real actions across your systems, with a person in the loop where it matters.

Customer service

Support agents that finish the job

Check the order, issue the refund, update the CRM, and hand over to a human when the case needs one.

BFSI

KYC and loan processing

Gather documents, verify details across systems and prepare the file for an underwriter, with a full audit trail.

Cyber security

SOC triage

Investigate alerts, enrich them with threat intelligence and suggest containment steps for an analyst to approve.

Healthcare

Care coordination

Book appointments, summarise records and prepare prior authorisation paperwork under strict access controls.

Manufacturing

Procurement and supply

Watch inventory, compare supplier quotes and raise purchase requests within approved policy limits.

IT operations

IT service desk

Reset access, provision resources and close common tickets through your ITSM integrations.

08 Questions

What architects usually ask us

Not covered here? Ask us directly. We would rather answer a specific question than send a brochure.

Ask a question

It is a set of modular AWS services for deploying and running AI agents securely at scale. The pieces are Runtime, Memory, Gateway, Identity, Policy, Evaluations, Code Interpreter, Browser and Observability. You can use any of them on their own or together.

Agents for Amazon Bedrock is a managed, configuration-based way to build an agent inside Bedrock. AgentCore is infrastructure for running any agent, built with any framework and any model. It gives developers more control for production workloads, and asks a bit more engineering in return.

It works with open-source frameworks such as Strands Agents, LangGraph, CrewAI and LlamaIndex, and with your own custom code. Agents can use foundation models from Amazon Bedrock or from other providers.

No. Each service is modular. You could use Gateway and Identity with agents hosted somewhere else, or run Runtime and Observability on their own and add the rest later.

Runtime isolates each user session. Identity manages agent and user credentials through your identity provider. Policy enforces fixed rules on what an agent can do with tools and data. On top of that we add threat modelling, guardrails and security testing.

It is consumption based with no upfront commitment. You pay for what each service uses, such as runtime compute, memory storage and gateway calls. We estimate costs during the architecture phase and keep tuning them once the agent is live.

AgentCore is available in multiple AWS Regions, including Asia Pacific Regions. During planning we check Region availability and any data residency requirements for your workloads.