Production AI agents, built to last.
AgentCore gives agents a secure runtime, memory, identity and tools. CloudZA® designs, ships and operates them on AWS — from a first proof-of-concept to an agent your business depends on, then we stay for support.
$ cloudza agentcore deploy --agent support-copilot ✓ Runtime provisioned · scale-to-zero ✓ Memory long-term store linked ✓ Identity least-privilege roles bound ✓ Gateway 6 tools exposed via MCP ✓ Observability traces streaming → agent live: prod · p95 1.2s
Seven building blocks. One production agent.
AgentCore is modular — use what you need. Here's each piece, and how CloudZA® puts it to work.
Runtime
A serverless runtime to deploy agents in any framework or model and scale them to zero.
Memory
Short- and long-term memory so agents recall context across turns and sessions.
Identity
Secure agent identity and least-privilege access to your tools and data.
Gateway
Turn your APIs and Lambda functions into agent-ready tools via MCP.
Browser
A managed, isolated browser so agents can navigate and act on the web.
Code Interpreter
A sandboxed environment for agents to write and run code on the fly.
Observability
End-to-end tracing and evals of every step an agent takes.
Bedrock models
Frontier and open models through Amazon Bedrock, governed in your account.
An agent is a configuration on a managed harness.
AgentCore separates what your agent is from what runs it. CloudZA® owns both — so the same agent behaves the same from a laptop experiment to production.
The declarative spec
Versioned and reviewed like code — model, prompt, tools and the guardrails on the loop.
The AgentCore primitives
The managed services CloudZA® wires in and operates — no agent infrastructure to run yourself.
The hard parts of agents, handled.
Teams stall when an agent meets reality — sessions, isolation, tenancy, tool selection. The harness takes that on so we ship.
Rapid prototyping
Stand an agent up in minutes — two parameters to start, everything else layered on later.
Personalization
Memory and per-user context so the agent adapts to each person it serves.
Multi-tenancy
Isolated sessions and scoped identity keep every tenant's data and actions cleanly apart.
Context & tool selection
The harness manages the agent loop — choosing the right tools and trimming context for you.
Coding agents
A sandboxed environment with shell and code execution, for agents that build — not just chat.
Long-running agents
Conversational and long-running workloads with sessions that live for hours, then clean themselves up.
From two parameters to production.
A harness needs only a name and an execution role to exist. CloudZA® layers on model, prompt, tools, memory and guardrails — as code, reviewed and repeatable.
# CloudZA® provisions the harness as code client = boto3.client("bedrock-agentcore-control") harness = client.create_harness( harness_name="support-copilot", executionRoleArn=role_arn, # only 2 required model={"bedrockModelConfig": {"modelId": "claude-sonnet"}}, systemPrompt={"text": "You are CloudZA®'s agent…"}, memory=memory_cfg, tools=[gateway_tools], skills={"path": skills_path}, )
model · systemPromptWhich Bedrock model the loop uses, and the agent's instructions.tools · skills · allowedToolsGateways, browser, code and reusable skills — scoped to what the agent may call.memoryShort- and long-term recall configuration.authorizerConfigurationAgent identity and least-privilege access.maxIterations · maxTokens · timeoutSecondsGuardrails on the agent loop and its cost.environment · environmentArtifactRuntime config, or bring your own container image.Runtime & session lifecycle
Every session runs in an isolated microVM with a managed lifecycle — so we never leave compute, or cost, running.
Hard isolation per session — the default image, or bring your own container from ECR (linux/arm64).
Idle timeout reclaims a session after inactivity, so nothing lingers.
Max lifetime caps long-running sessions; network and filesystem are configurable.
Direct shell access to the harness VM for pre/post scripts, environment setup or installing skills — not everything has to go through the agent loop.
Observability & evaluations trace every step and score quality before a change ships.
Most agent demos never ship. Ours run.
A real PoC in weeks
One use-case, your data, measured against a business metric — built on AgentCore Runtime and Bedrock.
Hardened for prod
Identity, memory, gateways, evals, guardrails and cost controls — Well-Architected, as code.
Watched & evolved
We run it with full observability, tune quality and cost, and ship the next capability — long term.
Let's prove an AgentCore use-case on AWS.
Run the readiness assessment, then a CloudZA® architect scopes an agent against a metric that matters — and takes it from PoC to production.