Your agent demo took a week. Production is taking six months.
It doesn't have to. See how teams are getting agents to production, quickly, and securely.
Sound familiar?
- of enterprise AI agent pilots never reach production
- 88%
- of enterprises will demote or decommission autonomous AI agents by 2027 due to governance failures
- 40%
- agents will operate inside the average Fortune 500 enterprise by 2028, fueling significant agent sprawl
- 150,000+
- of organizations cannot see the full scope of their AI spend
- 54%
Which of these is stalling you? Get expert help in an upcoming webinar.
Common problems show up in nearly every organization building and running agents at scale. Here's how to solve them.
The production gap
Months go into building identity, memory, and observability for one agent. Then the next team starts over. A managed platform gives you that foundation from day one, so you ship faster.
Governance blind spots
A working demo isn't a system you can rely on, and governance gaps surface only in production. Enforce what each agent can access and do, then trace and evaluate to prove how it behaved.
Agent sprawl
As more teams build, agents multiply faster than anyone can track. Without a central view, no one knows what exists, who owns it, or who has access. One registry gives you visibility to govern and reuse.
Unpredictable cost
Agent spend you can't attribute is agent spend you can't control. Track every agent related cost by team, project, and agent action. Grow usage knowing exactly what you're spending.
Lock-in fear
Build with leading models like Anthropic, OpenAI, and top open source options, using just one set of controls. Swap any model or framework without re-platforming, no matter how many agents you're running.
Isolated coding agents
Coding agents are powerful, but siloed on dev machines. Give them a cloud runtime and shared memory so they persist, share context, and follow your team's guidelines.
How leading teams are building and shipping agents
From startups to global enterprises, teams trust AWS to move agents from prototype to production at scale. See why.
Amazon Bedrock
The comprehensive platform to securely build and scale AI applications and agents
Start deploying AI agents at scale
Amazon Bedrock
The trusted place to build AI.
Amazon Bedrock AgentCore
Ship agents, not infrastructure.
Build AI agents that actually ship
Frequently asked questions
FAQs
Open allBuilding AI agents on AWS starts with selecting a model on Amazon Bedrock. Choose from leading frontier and open source models, including Anthropic and OpenAI. Then use Amazon Bedrock AgentCore to deploy your agent logic in any framework (LangGraph, CrewAI, Strands, OpenAI Agents SDK, or custom). AgentCore provides the managed runtime, memory, and tool connectivity so you focus on agent logic, not infrastructure.
Amazon Bedrock AgentCore is a managed platform that provides runtime, memory, identity, observability, and tool integration for AI agents. You deploy production agents by writing agent logic in any framework (LangGraph, CrewAI, Strands, OpenAI Agents SDK, or custom), connecting to models on Amazon Bedrock, and deploying to AgentCore's managed runtime with security enforced at the infrastructure layer from day one.
The production gap exists because operational infrastructure (runtime, memory, identity, security, observability) must be built before agents take a single action. AgentCore eliminates this by providing a managed platform with production-grade security enforced at the infrastructure layer. Teams go from local development to deployed agents without rearchitecting.
AgentCore enforces security at the infrastructure layer using Cedar policies evaluated on every tool call and verified by automated reasoning, the same technology behind Amazon S3 and AWS IAM. Credentials are managed at the gateway and never enter the agents loop. This provides deterministic enforcement that agents cannot reason their way around.
Agent and inference spend on AWS is attributed per agent by IAM principal, visible on your existing AWS bill. Amazon Bedrock offers prompt caching, intelligent routing, model distillation, and batch inference to optimize costs automatically. Per-agent attribution means you can forecast, budget, and explain AI spend the same way you manage any other workload; no separate AI billing system required.
Yes. AgentCore is model-neutral and framework-agnostic. Run frontier models from Anthropic, OpenAI, Amazon Nova, and others on Bedrock. Build agents with LangGraph, CrewAI, Strands, OpenAI Agents SDK, or custom frameworks. Swap models without refactoring your agents. The platform decouples at the infrastructure layer, not just the API.
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