How do I handle hallucinations in production agents?

Implement clear tool descriptions, ground truth datasets, automated evaluation that catches accuracy drops, and guardrails that validate outputs. AgentCore Evaluations monitors hallucination rates and triggers rollbacks if thresholds are exceeded.

Should I use one agent or multiple agents?

Start with one focused agent. If it has more than 15-20 tools or prompts exceeding 3000 tokens, consider splitting. Multiple focused agents are easier to debug, test, and maintain.

How do I manage costs as agents scale?

Track tokens per query from day one. Use deterministic code for calculations. Test cheaper models against your evaluation suite. Set budget alerts and rate limits per user/team.

What's the difference between MCP and Agent2Agent (A2A)?

MCP is for tool integration—defining how agents invoke tools. A2A is for agent-to-agent communication—defining how agents collaborate and hand off work.

How long does it take to move from prototype to production?

With AgentCore and proper practices, teams typically move from prototype to internal pilot in 4-6 weeks, then to controlled production in another 4-6 weeks. Start small and iterate based on real feedback.

Amazon Bedrock AgentCore: 9 Enterprise Best Practices

The gap between an AI agent demo that impresses leadership and one that actually runs in production is massive. At NexaFinance Corporation, the AI team built an impressive prototype in three weeks—a financial analyst assistant that could pull data, calculate metrics, and generate reports. Six months later, that prototype still wasn't in production. Why? They hadn't solved session isolation, couldn't debug why the agent sometimes chose wrong tools, and had no way to measure if changes made things better or worse.

Amazon Bedrock AgentCore provides the infrastructure layer that bridges this gap. It's not just about building agents—it's about deploying, monitoring, and scaling them across enterprises. This guide covers nine battle-tested practices for building production-grade AI agents, from initial scoping through organizational scaling, with practical examples you can implement immediately.

AgentCore Components:

AgentCore Runtime: Isolated execution environment for each session
AgentCore Gateway: Unified tool access with authentication
AgentCore Memory: Short-term and long-term context storage
AgentCore Observability: Tracing, metrics, and debugging
AgentCore Identity: Authentication and authorization

Practice 1: Start Small and Define Success Clearly

The biggest mistake teams make is building agents that try to handle everything. Instead of asking "what can this agent do?", ask "what specific problem are we solving?"

Define Four Deliverables Before Coding

Scope Document

Clear definition of what the agent should and should NOT do. Write it down, share with stakeholders, use it to reject feature creep.

Personality Guidelines

Tone, greeting style, how to handle out-of-scope questions. Formal vs. conversational, first name usage, escalation language.

Tool Definitions

Unambiguous descriptions for every tool, parameter, and knowledge source. Vague descriptions cause wrong tool selection.

Ground Truth Dataset

Expected interactions covering common queries and edge cases. The test data you'll use to evaluate every change.

Example: Sales Analytics Agent Scope

# Sales Analytics Agent - Scope Document

## SHOULD DO:
- Retrieve quarterly revenue by region (EMEA, APAC, AMER)
- Calculate growth metrics between periods
- Generate executive summaries for specific territories
- Compare performance across regions

## SHOULD NOT DO:
- Provide investment advice
- Access employee compensation data
- Execute trades or financial transactions
- Discuss individual sales rep performance

## PERSONALITY:
- Professional but conversational
- Address users by first name
- Acknowledge data limitations transparently
- State confidence level when uncertain
- Avoid financial jargon without explanation

## TOOLS:
getQuarterlyRevenue(region: EMEA|APAC|AMER, quarter: YYYY-QN)
calculateGrowth(current: number, previous: number)
getMarketData(region: string, dataType: revenue|sales|customers)

Practice 2: Instrument Everything from Day One

Don't treat observability as something to add later. By the time you realize you need it, you've already shipped an agent that's difficult to debug.

Three Layers of Observability

Layer	Purpose	Key Metrics
Trace-Level Debugging	See exact steps of each conversation	Tool calls, reasoning steps, response times
Production Dashboards	Aggregate performance monitoring	P50/P95 latency, error rates, throughput
Token & Cost Tracking	Budget management and optimization	Tokens per query, cost by team/agent

AgentCore services emit OpenTelemetry traces automatically. Export data to your existing observability stack:

# Configure OpenTelemetry export for AgentCore
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# Initialize tracer
provider = TracerProvider()
processor = BatchSpanProcessor(OTLPSpanExporter(
    endpoint="your-observability-platform.com:4317"
))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

# Traces now flow to Datadog, Dynatrace, LangSmith, or Langfuse

Practice 3: Build a Deliberate Tooling Strategy

Tools are how your agent accesses the real world. The quality of your tool definitions directly impacts agent performance.

Good vs Bad Tool Descriptions

# BAD - Forces agent to guess inputs and outputs
description = "Gets revenue data"

# GOOD - Removes ambiguity
description = """
Retrieves quarterly revenue data for a specified region and time period.
Returns values in millions of USD.
Requires region code (EMEA, APAC, AMER) and quarter in YYYY-QN format.
Example: getQuarterlyRevenue(region="EMEA", quarter="2024-Q3")
Returns: { "revenue": 142.5, "currency": "USD", "period": "2024-Q3" }
Error codes: 404 (region not found), 503 (data unavailable)
"""

Tip: Use Model Context Protocol (MCP) for tool reuse. Many providers offer MCP servers for Slack, Google Drive, Salesforce, and GitHub. Wrap internal APIs as MCP tools through AgentCore Gateway.

Four Pillars of Tool Strategy

Error Handling & Resilience

Define behavior for every failure mode—retry, fallback to cache, or tell user service is unavailable

Reuse via MCP

One protocol across all tools makes them discoverable by different agents

Centralized Tool Catalog

Security-reviewed, production-tested tools that teams can reuse

Code Examples

Working samples developers can copy and adapt—documentation alone isn't enough

Practice 4: Automate Evaluation from the Start

You need to know whether your agent is getting better or worse with each change. Automated evaluation provides this feedback loop.

Define Domain-Specific Metrics

# Evaluation Metrics for Sales Analytics Agent

evaluation_config = {
    "tool_selection_accuracy": {
        "description": "Did agent choose correct tool?",
        "target": 0.95,  # 95% accuracy
        "critical": True
    },
    "parameter_extraction": {
        "description": "Did agent extract correct parameters?",
        "target": 0.98,  # 98% accuracy
        "critical": True
    },
    "refusal_accuracy": {
        "description": "Did agent decline out-of-scope requests?",
        "target": 1.0,  # 100% - no exceptions
        "critical": True
    },
    "response_quality": {
        "description": "Clear explanation without jargon",
        "evaluator": "llm_as_judge",
        "target": 0.90
    },
    "latency_p50": {
        "target_ms": 2000,
        "critical": False
    },
    "latency_p95": {
        "target_ms": 5000,
        "critical": False
    },
    "tokens_per_query": {
        "target": 5000,
        "critical": False
    }
}

Build Comprehensive Test Datasets

# Test dataset should include multiple phrasings
test_cases = [
    # Standard phrasing
    {
        "query": "What's our Q3 revenue in EMEA?",
        "expected_tool": "getQuarterlyRevenue",
        "expected_params": {"region": "EMEA", "quarter": "2024-Q3"}
    },
    # Informal phrasing
    {
        "query": "How much did we make in Europe last quarter?",
        "expected_tool": "getQuarterlyRevenue",
        "expected_params": {"region": "EMEA", "quarter": "2024-Q3"}
    },
    # Abbreviated
    {
        "query": "EMEA Q3 numbers?",
        "expected_tool": "getQuarterlyRevenue",
        "expected_params": {"region": "EMEA", "quarter": "2024-Q3"}
    },
    # Out-of-scope - should refuse
    {
        "query": "What's the CEO's bonus?",
        "expected_behavior": "refuse",
        "reason": "compensation_data"
    }
]

Practice 5: Decompose Complexity with Multi-Agent Systems

When a single agent handles too many responsibilities, it becomes difficult to maintain. Decompose into specialized agents that collaborate.

Pattern	When to Use	Example
Sequential	Tasks have natural order	Data → Analysis → Report generation
Hierarchical	Need intelligent routing	Supervisor routes to HR, IT, or Finance agent
Peer-to-Peer	Dynamic collaboration without coordinator	Research agents share findings

Important: Protocols (A2A, MCP, HTTP) define how agents communicate. Patterns (Sequential, Hierarchical) define how they organize work. Keep these separate—don't couple infrastructure to business logic.

Practice 6: Scale Securely with Personalization

Moving from prototype to production serving thousands of users requires isolation, security, and personalization.

Security Architecture

# Security flow with AgentCore

┌─────────────┐     ┌─────────────────┐     ┌─────────────────┐
│    User     │────▶│ Identity Provider│────▶│ AgentCore       │
│  (Cognito,  │     │   (Auth Token)   │     │   Identity      │
│   Okta)     │     │                  │     │   (Claims)      │
└─────────────┘     └─────────────────┘     └────────┬────────┘
                                                      │
                                                      ▼
┌─────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   Tool      │◀────│ AgentCore       │◀────│ AgentCore       │
│  Execution  │     │   Gateway       │     │   Runtime       │
│             │     │  (Policy Check) │     │  (Isolated VM)  │
└─────────────┘     └─────────────────┘     └─────────────────┘

# Each session runs in isolated microVM
# AgentCore Policy validates user permissions before tool execution
# Gateway manages credentials for third-party services

Practice 7: Combine Agents with Deterministic Code

Not everything needs to be agentic. Reserve agents for reasoning over ambiguous inputs. Use traditional code for calculations and rule-based logic.

Use Agents For

Understanding natural language queries
Determining which tools to invoke
Interpreting results in context
Explaining findings to users

Use Code For

Mathematical calculations
Date validation and parsing
Business rule evaluation
Data formatting and transformation

# BAD: Exposing current date as agentic tool
# Results: 3 LLM calls, 4500 tokens, 3.2s latency

@tool
def get_current_date():
    return datetime.now().isoformat()

# GOOD: Pass date as context attribute
# Results: 2 LLM calls, 2800 tokens, 1.8s latency

agent.invoke(
    message="Create spending report for next month",
    context={
        "current_date": datetime.now().isoformat(),
        "user_timezone": "America/New_York"
    }
)

Practice 8: Establish Continuous Testing

Production isn't the finish line—it's the starting line. Agents operate in constantly changing environments. User behavior evolves, business logic changes, and model behavior can drift.

Testing Strategy Checklis:

Automated regression testing on every change
A/B testing for major updates (10% traffic initially)
Continuous sampling and evaluation in production
Drift detection with automated alerts
Automated rollbacks when quality degrades

Practice 9: Build Organizational Capability

Your first agent in production is an achievement. Enterprise value comes from scaling this capability across the organization with platform thinking.

Crawl → Walk → Run

Crawl Phase

Deploy first agent internally for small pilot group. Focus on learning and iteration. Failures are cheap.

Walk Phase

Expand to controlled external user group. More feedback, more edge cases. Investment in observability pays off.

Run Phase

Scale to all users with confidence. Platform enables other teams to build their own agents faster.

Frequently Asked Questions

Conclusion

Building enterprise AI agents isn't about the most sophisticated prompts or the latest model—it's about disciplined engineering practices, robust architecture, and continuous improvement. AgentCore provides the infrastructure: isolated runtimes, unified tool access, centralized observability, and enterprise security. The nine practices covered here give you the methodology.

Start small with clear scope. Instrument from day one. Build deliberate tooling. Automate evaluation. Decompose complexity. Scale securely. Combine agents with code. Test continuously. Build organizational capability. Each practice compounds the others—observability makes testing possible, testing enables confident scaling, and scaling creates organizational leverage.

Need Help Building Enterprise AI Agents?

Our AWS certified architects specialize in Amazon Bedrock implementations. We can help you scope your first agent, set up AgentCore infrastructure, establish evaluation pipelines, and scale AI capabilities across your organization.

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