{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"amazon-bedrock-agents","slug":"amazon-bedrock-agents","name":"Amazon Bedrock Agents","type":"agent","url":"https://aws.amazon.com/bedrock/agents","page_url":"https://unfragile.ai/amazon-bedrock-agents","categories":["ai-agents"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"amazon-bedrock-agents__cap_0","uri":"capability://planning.reasoning.multi.step.task.orchestration.with.agentic.reasoning","name":"multi-step task orchestration with agentic reasoning","description":"Bedrock Agents decomposes user requests into sequential task chains by leveraging foundation model reasoning to determine which actions to take and in what order. The agent maintains execution state across steps, allowing it to evaluate intermediate results and decide on next actions dynamically. This differs from simple prompt chaining by incorporating actual decision-making logic where the model determines task dependencies and branching paths based on real-time outcomes.","intents":["I want to build an agent that can break down a customer request into multiple API calls without hardcoding the workflow","I need an agent that can adapt its action sequence based on intermediate results from previous steps","I want to orchestrate complex business processes that require conditional logic and dynamic routing"],"best_for":["enterprise teams building customer service agents","developers creating autonomous workflow automation","organizations needing multi-step task decomposition without custom orchestration code"],"limitations":["Execution model (synchronous vs asynchronous) not documented — unclear if long-running workflows are supported","No visibility into reasoning chain or intermediate decision points — black-box orchestration","Maximum task depth and branching complexity not specified","Latency per orchestration step unknown — could be prohibitive for real-time applications"],"requires":["AWS account with Bedrock service access","IAM permissions for bedrock:InvokeAgent or equivalent","At least one foundation model available in Bedrock (model selection not documented)","Action groups defined (via Lambda or other compute)"],"input_types":["natural language instructions","structured task descriptions","user queries"],"output_types":["task execution results","structured action sequences","final agent response"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_1","uri":"capability://tool.use.integration.action.group.integration.with.lambda.based.function.calling","name":"action group integration with lambda-based function calling","description":"Bedrock Agents integrates with AWS Lambda functions through action groups, enabling the agent to invoke arbitrary business logic and external APIs. The agent generates function calls based on its reasoning about which actions are needed, passes parameters inferred from user intent, and receives structured results back into the reasoning loop. This creates a bridge between LLM reasoning and deterministic backend systems without manual prompt engineering for tool use.","intents":["I want my agent to call specific Lambda functions based on what it determines is needed","I need to connect my agent to existing AWS infrastructure and microservices","I want the agent to execute business logic (database queries, API calls, transactions) as part of its reasoning"],"best_for":["AWS-native teams with existing Lambda infrastructure","organizations with microservices architectures","enterprises needing to connect agents to proprietary backend systems"],"limitations":["Action group schema format and validation rules not documented","No support for non-Lambda compute (ECS, Fargate, external APIs) explicitly mentioned","Parameter inference mechanism not detailed — unclear how agent maps user intent to function arguments","Error handling and retry logic for failed Lambda invocations not specified","Cold start latency from Lambda not addressed in documentation"],"requires":["AWS Lambda functions deployed and accessible","IAM role with permissions to invoke Lambda from Bedrock","Action group definitions with function schemas","Bedrock agent with action group configuration"],"input_types":["function schemas (OpenAPI-like specifications)","user intent/natural language","structured parameters"],"output_types":["Lambda invocation results","structured function responses","error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_10","uri":"capability://tool.use.integration.aws.service.integration.and.enterprise.system.connectivity","name":"aws service integration and enterprise system connectivity","description":"Bedrock Agents integrates with AWS services and enterprise systems through action groups and Lambda functions, enabling agents to interact with databases, storage, messaging, and other AWS infrastructure. This allows agents to perform real business operations (querying databases, updating records, triggering workflows) as part of their task execution. The integration is mediated through Lambda, providing a flexible abstraction layer for connecting to any backend system.","intents":["I want my agent to query databases and retrieve real data for decision-making","I need the agent to update records in our systems as part of task execution","I want to connect my agent to existing AWS infrastructure without custom integration code"],"best_for":["AWS-native enterprises with existing infrastructure","organizations with microservices architectures","teams needing agents to perform real business operations"],"limitations":["Supported AWS services not explicitly documented","Non-AWS system integration approach not detailed","Transaction handling and consistency guarantees not specified","Error handling and rollback mechanisms not documented","Audit logging and compliance tracking not detailed"],"requires":["AWS Lambda functions for backend integration","IAM roles with appropriate service permissions","Action groups configured with Lambda invocation details","Backend systems accessible from Lambda execution environment"],"input_types":["user requests","query parameters","transaction data"],"output_types":["database query results","transaction confirmations","system state updates"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_11","uri":"capability://automation.workflow.agent.performance.monitoring.and.observability","name":"agent performance monitoring and observability","description":"Bedrock Agents integrate with AWS CloudWatch and X-Ray for monitoring agent invocations, tracking latency, action execution, and error rates. Provides metrics on agent reasoning steps, action invocations, and guardrail violations. Enables debugging of agent behavior through execution traces and logs without custom instrumentation.","intents":["I want to monitor agent performance and identify bottlenecks","I need to debug why an agent made a specific decision or failed","I want to track agent usage and costs across my organization"],"best_for":["teams operating agents in production requiring observability","organizations needing to optimize agent performance and costs","teams debugging agent behavior and reasoning"],"limitations":["CloudWatch metrics have standard AWS latency (1-5 minute delay)","X-Ray tracing adds overhead; may impact agent latency","Detailed reasoning traces may contain sensitive information; requires careful log retention policies","No built-in alerting for agent failures; requires CloudWatch alarms configuration","Cost tracking requires manual analysis of CloudWatch metrics"],"requires":["AWS CloudWatch enabled for Bedrock","AWS X-Ray enabled for tracing (optional but recommended)","IAM permissions for CloudWatch and X-Ray access","CloudWatch dashboard or third-party monitoring tool for visualization"],"input_types":["agent invocation metrics","action execution traces","error logs"],"output_types":["CloudWatch metrics (latency, invocation count, errors)","X-Ray traces showing execution flow","detailed logs with reasoning steps"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_2","uri":"capability://memory.knowledge.retrieval.augmented.generation.with.knowledge.base.integration","name":"retrieval-augmented generation with knowledge base integration","description":"Bedrock Agents can augment its reasoning and responses by retrieving relevant information from connected knowledge bases before and during task execution. The agent automatically determines when to query the knowledge base, retrieves semantically relevant documents or data, and incorporates retrieved context into its reasoning for more accurate and grounded responses. This enables agents to answer questions and make decisions based on company-specific data without fine-tuning.","intents":["I want my agent to answer questions using our internal documentation and knowledge base","I need the agent to retrieve relevant company data before making decisions or recommendations","I want to ground agent responses in actual company information rather than relying on model training data"],"best_for":["customer support teams building knowledge-grounded agents","enterprises with large internal documentation repositories","organizations needing compliance-aware responses based on specific policies"],"limitations":["Knowledge base indexing and retrieval mechanism not documented — unclear if vector embeddings, keyword search, or hybrid approach is used","Maximum knowledge base size and retrieval latency not specified","No documentation on how agent decides when to retrieve vs. when to use model knowledge","Supported knowledge base formats and data sources not detailed","Relevance ranking and retrieval quality metrics not provided"],"requires":["Knowledge base created and indexed in Bedrock","Documents or data ingested into knowledge base","Knowledge base connected to agent configuration","Appropriate IAM permissions for knowledge base access"],"input_types":["user queries","documents (PDF, text, structured data)","knowledge base indices"],"output_types":["retrieved documents/passages","augmented agent responses","grounded reasoning with citations"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_3","uri":"capability://memory.knowledge.session.based.conversation.memory.and.context.retention","name":"session-based conversation memory and context retention","description":"Bedrock Agents maintains conversation state and context across multiple turns within a session, allowing the agent to reference previous interactions, build on prior decisions, and maintain coherent multi-turn conversations. The agent automatically manages session context without requiring explicit memory management code, enabling natural conversational flows where the agent remembers user preferences, previous requests, and conversation history.","intents":["I want my agent to remember what the user asked in previous turns and reference it","I need multi-turn conversations where the agent builds context over time","I want to avoid re-explaining context in each new request to the agent"],"best_for":["conversational AI applications requiring context continuity","customer service agents handling multi-turn support interactions","interactive applications where user state matters across requests"],"limitations":["Session lifetime and maximum context window not documented","Memory persistence mechanism not specified — unclear if sessions are ephemeral or persistent","No documentation on how context is pruned or summarized for long conversations","Session isolation and multi-user handling not detailed","Cost implications of maintaining session state not addressed"],"requires":["Session ID management (Bedrock-provided or custom)","Agent configured with session support","Mechanism to pass session context between requests"],"input_types":["user messages","session identifiers","conversation history"],"output_types":["contextual agent responses","session state updates","conversation history"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_4","uri":"capability://safety.moderation.guardrails.based.content.filtering.and.safety.constraints","name":"guardrails-based content filtering and safety constraints","description":"Bedrock Agents includes built-in guardrails that enforce safety policies, content filtering, and compliance constraints on both agent inputs and outputs. The guardrails operate as a policy layer that can block, modify, or flag requests and responses based on configurable rules without requiring custom filtering logic. This enables organizations to enforce brand safety, compliance requirements, and content policies consistently across all agent interactions.","intents":["I want to prevent my agent from generating harmful, offensive, or non-compliant content","I need to enforce company policies on what the agent can discuss or recommend","I want to block certain types of requests (e.g., requests for sensitive data) before the agent processes them"],"best_for":["regulated industries (finance, healthcare, legal) requiring compliance enforcement","enterprises with strict brand safety requirements","organizations needing consistent content policy enforcement across agents"],"limitations":["Specific guardrail policies and configurable constraints not documented","No documentation on custom rule definition or policy syntax","Guardrail effectiveness metrics and false positive rates not provided","Performance impact of guardrail evaluation not specified","Interaction between guardrails and agent reasoning not detailed — unclear if guardrails can cause agent failures"],"requires":["Bedrock Guardrails service enabled","Guardrail policies defined and configured","Agent configured to use specific guardrails","IAM permissions for guardrails evaluation"],"input_types":["user requests","agent-generated responses","policy definitions"],"output_types":["filtered/modified content","policy violation flags","compliance attestations"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_5","uri":"capability://automation.workflow.return.of.control.with.agent.handoff.and.human.in.the.loop","name":"return of control with agent handoff and human-in-the-loop","description":"Bedrock Agents supports returning control to the calling application at specific decision points, enabling human-in-the-loop workflows where agents can escalate to humans, request approval for high-stakes actions, or pause for external input. The agent can signal when it needs human intervention, provide context about why intervention is needed, and resume execution after receiving human feedback or approval. This creates hybrid workflows combining autonomous agent capabilities with human oversight.","intents":["I want my agent to ask for human approval before executing high-risk actions (e.g., large transactions)","I need to escalate complex or ambiguous requests to human agents","I want to implement approval workflows where agents prepare actions but humans execute them"],"best_for":["financial services requiring transaction approval workflows","customer service with escalation to human agents","high-stakes decision-making requiring human oversight"],"limitations":["Return of control trigger conditions and API not documented","No specification of how context is passed to human reviewers","Resume mechanism after human input not detailed","Timeout handling for pending human decisions not specified","Audit trail and decision logging not documented"],"requires":["Agent configured with return-of-control checkpoints","Calling application capable of handling control returns","Human review interface or workflow system","Mechanism to resume agent execution after human decision"],"input_types":["agent execution state","context for human review","human decisions/approvals"],"output_types":["control return signals","context for human review","resumed execution results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_6","uri":"capability://text.generation.language.foundation.model.selection.and.inference.routing","name":"foundation model selection and inference routing","description":"Bedrock Agents runs on Bedrock foundation models, abstracting away model selection and inference details. The agent can leverage different foundation models for reasoning, with Bedrock handling model availability, versioning, and inference routing. The documentation mentions 'Bedrock Managed Agents, powered by OpenAI' as a separate offering, suggesting multiple model options, though specific model names, versions, and selection criteria are not documented.","intents":["I want to use a specific foundation model (e.g., Claude, GPT) for my agent without managing inference infrastructure","I need to switch between models without changing agent code","I want to leverage the best available model for my use case without infrastructure concerns"],"best_for":["teams wanting managed model inference without infrastructure setup","organizations evaluating multiple foundation models","enterprises needing model flexibility without operational overhead"],"limitations":["Available foundation models not documented — unclear which models are supported","Model selection mechanism not specified — no documentation on how to choose between models","Model versioning and update policies not documented","Inference latency and throughput characteristics per model not provided","Cost differences between models not specified","Context window sizes and token limits per model not documented"],"requires":["Bedrock service access with foundation models enabled","Model availability in agent's AWS region","Appropriate IAM permissions for model inference"],"input_types":["agent instructions","user requests","model selection parameters (if applicable)"],"output_types":["model inference results","agent responses"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_7","uri":"capability://planning.reasoning.agent.instruction.and.behavior.customization","name":"agent instruction and behavior customization","description":"Bedrock Agents allows customization of agent behavior through instructions that define the agent's role, constraints, and decision-making guidelines. These instructions shape how the agent interprets requests, prioritizes actions, and generates responses without requiring model fine-tuning. The instruction-based approach enables rapid iteration on agent behavior and persona without retraining or code changes.","intents":["I want to define my agent's role and expertise area (e.g., 'customer support specialist')","I need to set constraints on what the agent can do or recommend","I want to customize the agent's tone, style, and decision-making approach"],"best_for":["teams rapidly iterating on agent behavior","organizations needing multiple specialized agents with different roles","non-technical users customizing agent personality without code"],"limitations":["Instruction syntax and formatting requirements not documented","Maximum instruction length and complexity not specified","No guidance on instruction effectiveness or best practices","Interaction between instructions and guardrails not detailed","Instruction versioning and rollback mechanisms not documented"],"requires":["Agent creation interface or API","Instruction text defining agent behavior","Bedrock agent configuration"],"input_types":["natural language instructions","behavior definitions","constraint specifications"],"output_types":["customized agent behavior","instruction-guided responses"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_8","uri":"capability://code.generation.editing.code.interpretation.and.execution.capability","name":"code interpretation and execution capability","description":"Bedrock Agents includes code interpretation capabilities, allowing agents to analyze, generate, and potentially execute code as part of task completion. This enables agents to solve programming problems, debug code, generate scripts, and perform computational tasks that require code understanding. The exact scope of code execution (interpreted vs. sandboxed execution) and supported languages are not documented.","intents":["I want my agent to help debug or review code","I need the agent to generate code snippets or scripts for specific tasks","I want the agent to perform computational analysis or data transformation using code"],"best_for":["developer-facing agents providing coding assistance","automation agents that need to generate or analyze code","technical support agents helping with programming issues"],"limitations":["Supported programming languages not documented","Code execution model not specified — unclear if code is interpreted, sandboxed, or analyzed only","Security constraints and code execution limits not detailed","Maximum code length and complexity not specified","Error handling for malformed or dangerous code not documented"],"requires":["Agent configured with code interpretation capability","Appropriate security policies if code execution is enabled"],"input_types":["code snippets","programming questions","debugging requests"],"output_types":["generated code","code analysis results","execution results"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__cap_9","uri":"capability://planning.reasoning.multi.agent.collaboration.and.supervisor.orchestration","name":"multi-agent collaboration and supervisor orchestration","description":"Bedrock Agents supports multi-agent architectures where multiple specialized agents coordinate under a supervisor agent. The supervisor agent can delegate tasks to specialized agents, aggregate results, and make higher-level decisions based on agent outputs. This enables decomposition of complex problems into specialized sub-agents while maintaining coherent overall task execution.","intents":["I want to build a system with specialized agents (e.g., research agent, analysis agent, writing agent) coordinated by a supervisor","I need agents to collaborate on complex tasks by delegating to specialists","I want to decompose complex workflows into independent agent capabilities"],"best_for":["enterprises with complex multi-domain workflows","teams building specialized agent networks","organizations needing modular, reusable agent components"],"limitations":["Multi-agent communication protocol not documented","Supervisor agent decision-making logic not specified","Agent-to-agent result passing and format not detailed","Failure handling and retry logic for sub-agents not documented","Latency implications of multi-agent coordination not addressed","Maximum number of coordinated agents not specified"],"requires":["Multiple agents created and configured","Supervisor agent with delegation logic","Communication mechanism between agents","Shared context or result passing mechanism"],"input_types":["user requests","task decompositions","agent outputs"],"output_types":["delegated tasks","aggregated results","final supervisor response"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"amazon-bedrock-agents__headline","uri":"capability://automation.workflow.ai.managed.task.orchestration.agents","name":"ai-managed task orchestration agents","description":"Amazon Bedrock Agents are AWS-managed agents designed to automate and orchestrate multi-step tasks using foundation models, enabling seamless execution of complex workflows with built-in guardrails and session management.","intents":["best AI task orchestration agents","AI agents for automating workflows","top multi-step task automation tools","AI agents for enterprise process management","best AWS agents for task execution"],"best_for":["business process automation","complex task management"],"limitations":["requires AWS infrastructure"],"requires":["AWS account"],"input_types":["user instructions"],"output_types":["executed tasks","task results"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["AWS account with Bedrock service access","IAM permissions for bedrock:InvokeAgent or equivalent","At least one foundation model available in Bedrock (model selection not documented)","Action groups defined (via Lambda or other compute)","AWS Lambda functions deployed and accessible","IAM role with permissions to invoke Lambda from Bedrock","Action group definitions with function schemas","Bedrock agent with action group configuration","AWS Lambda functions for backend integration","IAM roles with appropriate service permissions"],"failure_modes":["Execution model (synchronous vs asynchronous) not documented — unclear if long-running workflows are supported","No visibility into reasoning chain or intermediate decision points — black-box orchestration","Maximum task depth and branching complexity not specified","Latency per orchestration step unknown — could be prohibitive for real-time applications","Action group schema format and validation rules not documented","No support for non-Lambda compute (ECS, Fargate, external APIs) explicitly mentioned","Parameter inference mechanism not detailed — unclear how agent maps user intent to function arguments","Error handling and retry logic for failed Lambda invocations not specified","Cold start latency from Lambda not addressed in documentation","Supported AWS services not explicitly documented","builder identity is not verified yet","no observed match outcomes 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