Human-in-the-loop claims processing with Amazon Bedrock AgentCore

Each state eligibility office we talk to asks the same question: How do we move faster without increasing errors? The agentic AI pitch sounds compelling. The agent handles everything, humans step back, costs drop. But for environments where a bad decision means a family loses benefits or a state faces federal penalties, that pitch skips a few chapters.

States face financial sanctions when Supplemental Nutrition Assistance Program (SNAP) determination error rates exceed 6%. A single misclassified household can trigger corrective action cascading across a program. These stakes are budgetary and concrete, not abstract.

Here’s the argument the autonomous agent pitch avoids: Most production deployments don’t start fully autonomous, and many won’t. We recommend that the architecture supports the reality by design. Amazon Bedrock AgentCore developed by Amazon Web Services (AWS) does.

In this post, we explore a human-in-the-loop (HITL) framework in the form of architectural patterns teams can use to introduce agentic AI incrementally into eligibility workflows that are adapted to their programs, policies, and risk tolerances.

The autonomy progression model

Teams deploying agentic AI for claims processing tend to fall on a spectrum. At one end, the agent drafts and a human approves everything. At the other, the agent decides autonomously on scoped case types with proven accuracy. The middle ground is supervised autonomy, and that’s where most production systems live for a long time. Trust is earned through measured accuracy, not declared by architecture.

In this middle framework, Assist mode is where the agent drafts determinations and surfaces evidence. A caseworker reviews and approves each one. This is where you build trust, measure accuracy, and establish your baseline error rate.

In Supervised autonomy, the agent handles routine cases independently. Edge cases, denials, and anything below a given confidence threshold still route to humans.

Cedar policies define the boundary between Assist and Supervised autonomy modes declaratively. Cedar is an open source policy language developed by AWS where you can write human-readable rules specifying what an agent is permitted or forbidden to do. Instead of burying authorization logic in application code, you express it as policy: If conditions A, B, and C are true, permit this action. If condition D is true, forbid it. The rules are auditable, able to be versioned, and separate from the agent itself.

In Full autonomy (selective), only case types where the agent has demonstrated sustained accuracy at or below the acceptable error rate qualify. You can expand the scope based on evidence, not ambition.

How Amazon Bedrock AgentCore supports the spectrum

A claims processing agent needs multiple capabilities working together. Here’s what matters, grouped by the question each capability answers:

Know: What policy applies?

Amazon Bedrock Knowledge Bases ingests federal regulations, state manuals, and county procedures. The agent retrieves actual policy language rather than approximating it. Amazon Bedrock AgentCore Memory persists context across sessions so the agent doesn’t start from zero on a reopened case. The case history, redetermination timelines, and pending verifications are all carried forward.

Decide: What action is authorized?

Authorization boundaries must be explicit, not implied. Cedar policies make them declarative. Amazon Bedrock AgentCore Runtime reasons about the determination in an isolated environment. Cedar policies define what the agent can and can’t do. For example: Calculate eligibility, yes. Override a human denial, no. Modify policy rules, no. Access cases outside its caseload, no. Cedar also scopes authority per program. This can apply to decisions such as authorizing the agent for making SNAP determinations, but making it recommend-only for Medicaid.

Connect: How does it reach external systems?

Amazon Bedrock AgentCore Gateway routes tool calls through a single managed entry point, such as case management systems, document services, state wage databases, and federal verification hubs. Amazon Bedrock Guardrails helps prevent personally identifiable information (PII) leakage, block prompt injection, and enforce topic boundaries.

Trust: How do humans stay in control?

Oversight is the mechanism that makes autonomy possible. Amazon Bedrock AgentCore Observability tracks each determination through the full reasoning chain. Evaluation scores decisions against ground truth and can detect drift within days. AWS Step Functions embeds agentic reasoning inside workflows with human approval gates. AWS Lambda interceptors evaluate each action in real time and escalate dynamically.

Three human-in-the-loop mechanisms

This framework provides three patterns for keeping humans in the loop at the right points in a claims workflow.

1. Cedar policies as approval gates

The same policy that grants autonomous authority on routine cases can require human sign-off on exceptions. The agent does the cognitive work by gathering evidence, calculating eligibility, and drafting a determination, but the final action waits for confirmation when the policy demands it. Cedar’s forbid-beats-permit semantics make this a direct expression of policy rather than a workaround.

The following Cedar policy example shows how you could scope autonomous approval for routine SNAP renewals while requiring human review when income changes are detected:

// Permit autonomous processing for routine SNAP renewals
// where no income change is detected and confidence is high
permit (
  principal == EligibilityAgent::"snap-claims-agent",
  action == Action::"approve_determination",
  resource is Case
)
when {
  resource.program == "SNAP" &&
  resource.case_type == "renewal" &&
  resource.income_change_detected == false &&
  context.confidence_score >= 0.92
};
 
// Forbid autonomous approval when income has changed.
// This forces the case to a human reviewer.
forbid (
  principal == EligibilityAgent::"snap-claims-agent",
  action == Action::"approve_determination",
  resource is Case
)
when {
  resource.income_change_detected == true
};

In this example, the permit policy allows the agent to approve routine SNAP renewals only when no income change is detected and the model confidence score meets the 92% threshold. The forbid policy overrides any permit when income has changed, routing the case to a human caseworker. Because Cedar uses forbid-beats-permit semantics, a single forbid policy is sufficient to block autonomous action regardless of other permits in the policy set.

2. AWS Step Functions for workflow-level control

In this pattern, the agent handles high-volume cognitive tasks such as evidence gathering, eligibility calculation, and document assembly. The workflow pauses at a defined checkpoint. A human reviews the determination. Execution continues only after approval. You get AI throughput on low-risk work and human judgment where it matters.

3. AWS Lambda interceptors for dynamic escalation (design pattern)

Unlike Cedar policies and AWS Step Functions, which are product features you configure, AWS Lambda interceptors are an architectural design pattern you implement yourself. You write AWS Lambda functions that sit in the agent’s action path and evaluate each action in real time.

This provides fine-grained, dynamic escalation logic without hard-coding each edge case. For example, if the agent is about to deny benefits to a household with children under 5, escalate to a supervisor. These rules incorporate context the agent itself surfaces, giving teams dynamic escalation logic that adapts as policy evolves.

Why this matters now

Caseloads outpace hiring year over year. Caseworker turnover exceeds 30% in some states according to workforce surveys and federal research. Policy complexity increases each legislative session. Missed redetermination deadlines mean families lose coverage, agencies face audit findings, and program integrity erodes.

The framework discussed in this post gives caseworkers an agent that handles the research, retrieval, calculation, and documentation so human judgment goes toward the cases that actually need it.

An agent designed to process the majority of routine redeterminations autonomously while routing exceptions, denials, and low-confidence cases to caseworkers is a force multiplier. It helps keep error rates within federal tolerance while clearing the backlog that causes missed deadlines. Families can get answers faster, caseworkers can spend time on cases that need human judgment, and agencies can maintain the public trust that makes these programs work.

Get started

Ready to explore how HITL patterns can apply to your agency’s eligibility workflows? Start with these three components:

1. Amazon Bedrock AgentCore – Build, deploy, and manage AI agents with runtime isolation and observability.
2. Cedar policy language – Declarative authorization for fine-grained agent permissions. Use Cedar to define what your agent can and can’t do per program and case type.
3. AWS Step Functions – Embed agentic reasoning inside HITL workflows. Design checkpoints where human review is required before the agent proceeds.

You can contact your AWS account team to discuss a pilot, or visit the Amazon Bedrock AgentCore Documentation to start building.