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About two in ten respondents to McKinsey’s 2026 State of AI survey report having scaled AI agents across their organizations, while most are still working to move beyond the demo stage. Amazon Bedrock AgentCore, AWS’s managed platform for securely running AI agents at scale, helps bridge this gap. While AgentCore provides the runtime, identity, memory and tooling capabilities, the engineering decisions around implementation determine whether agents can perform reliably in production. Here are four critical challenges enterprises should address before scaling.
Why Moving Amazon Bedrock AgentCore from Prototype to Production Is Challenging
A prototype serves one user, one tool and a forgiving audience. In production, agents must handle concurrent sessions, sensitive data, slow downstream systems and scrutiny over their decisions. These demands introduce challenges that go beyond model performance and require a more robust engineering approach. Following Amazon Bedrock AgentCore best practices means taking a phased approach to production deployment, validating security, reliability and performance at each stage before expanding workloads.
Challenge 1- Designing Scalable Agent Runtime and Session Management
AI agents maintain state, wait for tool responses and often run longer than typical web requests. AgentCore Runtime isolates sessions, keeping user contexts separate. For long-running workflows, Runtime Instances provide persistent compute for up to 14 days, supporting specialized hardware and multi-agent collaboration.
Decide session lifetime, concurrency limits and timeouts early, and choose what persists in AgentCore Memory instead of the prompt. Make tool calls idempotent so a retry does not repeat a payment or a ticket. Scaling production-ready AI agents on AWS requires designing for concurrency from the outset. An architecture that handles ten simultaneous sessions may not hold up under ten thousand.
Challenge 2- Securing Agent Identity, Tool Access and Enterprise Integrations
An agent that can call tools can cause damage at machine speed. AgentCore security best practices begin with identity. Give each agent its own identity through AgentCore Identity and avoid shared credentials.
Route tool access through AgentCore Gateway so calls pass one control point. AgentCore Policy intercepts tool calls and checks them against Cedar rules. In August 2026 AWS added temporal policies that evaluate an agent’s history of actions within a session. Amazon Bedrock AgentCore security and access control best practices include least-privilege IAM roles, KMS encryption and private connectivity to enterprise systems. High-impact actions, such as payments or record changes, should require human approval.
Challenge 3- Building AgentCore Observability and Continuous Evaluation
Uptime monitoring cannot show that an agent chose the wrong tool. AgentCore observability produces traces, metrics and logs through Amazon CloudWatch and OpenTelemetry-compatible tooling, showing each reasoning step, tool call and delay.
How to monitor AI agents using Amazon Bedrock AgentCore comes down to alerting on task success, tool errors, latency and token usage, in addition to uptime. How to evaluate AI agent reliability on AWS needs repeatable tests. AgentCore Evaluations includes 13 built-in evaluators for areas such as helpfulness, tool selection and accuracy, with custom scoring available. Run them on a fixed test set before each release and sample live traffic afterward.
Challenge 4- Managing Reliability, Governance and Costs at Scale
Production-ready AI agents on AWS need failure plans. Set timeouts, limited retries and fallbacks, and test what happens when a model or tool slows down. Version prompts, tools and policies together and keep a tested rollback.
Governance needs an owner for each agent, an approval path for new tools and a record of what each agent did. Cost grows with tokens, tool calls and session time, so budget per task and review usage monthly. Multi-step workflows consume far more tokens than a single model call, which makes cost per completed task a better measure than price per token.
How Does Forgeahead’s AWS Engineering Expertise Support Production-Ready AI Agents?
Forgeahead builds agentic systems on Amazon Bedrock that integrate with existing enterprise applications, data sources and workflows. Our approach combines agent orchestration, security, observability and cost management to help organizations move beyond experimentation and deploy AI agents reliably. By embedding these capabilities into the architecture from the start, we help enterprises build systems that can evolve as adoption grows.
Agent orchestration with narrow roles
Forgeahead structures agentic workflows around clearly defined responsibilities, assigning each agent a specific role and limiting its scope of action. This reduces unnecessary overlap and makes individual agents easier to test and manage. As workflows expand to involve multiple agents and tools, this structured approach helps maintain predictable execution and makes it easier to identify where an issue originates.
Scoped identity and encryption
As agents interact with enterprise applications and sensitive data, controlling what each one can access becomes increasingly important. Forgeahead implements least-privilege IAM roles for individual agents and tools, alongside AWS KMS encryption to protect sensitive information. These controls establish consistent security boundaries across the architecture, reducing the risk of excessive permissions as new agents and integrations are introduced.
Traceability and quality reviews
Understanding what an agent did, which tools it accessed and where its execution failed is essential for production operations. Forgeahead uses Amazon CloudWatch and AWS CloudTrail to establish visibility into agent activity, operational performance and access events. Regular prompt reviews and output evaluations complement this monitoring by identifying hallucinations, inconsistent responses and unexpected behavior, giving engineering teams a basis for continuous improvement.
Model routing and cost reviews
Not every agent task requires the same model or level of computational capability. Forgeahead incorporates model abstraction layers to route workloads across suitable models, balancing response quality, latency and inference costs. Monthly reviews of token consumption, model usage and resource allocation then help identify where workloads can be optimized, keeping AI operating costs aligned with business requirements as adoption increases.
Conclusion
Amazon Bedrock AgentCore simplifies much of the underlying infrastructure, but successful deployment still depends on sound engineering decisions. Establishing session limits, scoped identity, continuous evaluation and per-task cost tracking early helps create a reliable foundation for scaling AI agents. Forgeahead brings these capabilities together through its AWS engineering expertise, helping enterprises prepare agentic systems for production.
Which agent in your pipeline is closest to production? Speak with Forgeahead’s experts to identify the remaining gaps and plan your next steps.
Frequently Asked Questions
1. What should enterprises evaluate before adopting Amazon Bedrock AgentCore?
Assess the use cases, data sensitivity, identity provider integration and which AgentCore features are generally available versus in preview in your regions. Check how agents will connect to enterprise systems, who owns each agent and how the consumption-based pricing compares with your expected session volume over the first year. Pilot one bounded workflow before committing to a broader program.
2. How do you secure AI agents built with Amazon Bedrock AgentCore?
Give each agent its own identity, route tool calls through a gateway with policy checks, and scope IAM roles to the minimum each agent needs. Encrypt data with KMS, use private connectivity to internal systems and require human approval for high-impact actions. Keep logs of tool calls so reviewers can reconstruct what an agent did.
3. What are the key infrastructure requirements for enterprise AgentCore deployments?
Expect a multi-account structure with separate environments, identity provider integration, private network paths to internal systems, CloudWatch dashboards and alarms, and CI/CD pipelines for agent code, prompts and policies. Also plan service quotas, data stores for memory and retrieval, and a defined process for onboarding new tools safely and consistently.
4. How can enterprises control the cost of running AI agents at scale?
Budget per completed task and track tokens, tool calls and session time against it. Cap session duration and context size, route simple tasks to smaller models and cache repeated lookups. Set alarms on spend by agent, and hold a monthly review so unexpected growth in usage is caught early and explained to finance.
5. How does Amazon Bedrock AgentCore fit into existing enterprise AWS environments?
It works with familiar controls, including IAM, KMS, VPC connectivity, CloudWatch and organization-level policies. Place agents in dedicated accounts for each environment, expose existing APIs and Lambda functions as governed tools, and reuse your identity provider. Check feature availability in your regions, since some capabilities may still be in preview and need extra review.
6. When is Amazon Bedrock AgentCore the right choice for an enterprise AI strategy?
It fits when several agents need shared identity, memory, tool access and monitoring, or when session volume makes self-managed infrastructure costly to operate. A single simple workflow may not need it. Weigh the platform against framework and model flexibility, preview status of needed features and your staff’s capacity to operate alternatives.
