{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-humanlayer--12-factor-agents","slug":"humanlayer--12-factor-agents","name":"12-factor-agents","type":"repo","url":"https://github.com/humanlayer/12-factor-agents","page_url":"https://unfragile.ai/humanlayer--12-factor-agents","categories":["frameworks-sdks","deployment-infra"],"tags":["12-factor","12-factor-agents","agents","ai","context-window","framework","llms","memory","orchestration","prompt-engineering","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-humanlayer--12-factor-agents__cap_0","uri":"capability://tool.use.integration.natural.language.to.structured.tool.call.translation","name":"natural-language-to-structured-tool-call-translation","description":"Translates unstructured natural language agent reasoning into deterministic, schema-validated tool calls by implementing a strict separation between LLM reasoning and tool invocation. The system uses structured output formats (likely JSON schema validation) to ensure every tool call conforms to a predefined interface before execution, preventing hallucinated or malformed function calls from reaching production code. This implements Factor 1 of the 12-Factor methodology, treating tool calls as the primary interface between LLM decisions and deterministic system behavior.","intents":["I need to ensure my agent's tool calls are always valid and executable, not just plausible-sounding","I want to decouple LLM reasoning from actual system mutations to maintain auditability","I need to validate tool call schemas before they reach my business logic"],"best_for":["teams building production LLM agents that need strict tool call validation","developers migrating from free-form prompt-based tool calling to schema-driven approaches","organizations requiring audit trails of agent decisions vs actual system actions"],"limitations":["Requires explicit schema definition for every tool, adding upfront design overhead","Schema validation adds latency per tool call (~50-200ms depending on complexity)","Does not handle tools with highly dynamic or user-defined signatures without schema regeneration"],"requires":["TypeScript or Python runtime","JSON Schema or equivalent structured output format support","LLM provider with function calling or structured output API (OpenAI, Anthropic, etc.)"],"input_types":["natural language agent reasoning","tool schema definitions (JSON Schema format)","LLM function calling responses"],"output_types":["validated tool call objects","execution-ready function invocations","schema validation error reports"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_1","uri":"capability://text.generation.language.prompt.ownership.and.versioning.system","name":"prompt-ownership-and-versioning-system","description":"Provides a framework for treating prompts as first-class, versioned artifacts rather than embedded strings, enabling teams to own, test, and iterate on prompts independently from application code. Implements Factor 2 by establishing a clear separation between prompt templates, system instructions, and dynamic context injection, with support for prompt versioning, A/B testing, and rollback capabilities. Prompts are stored and managed as configuration rather than hardcoded, allowing non-engineers to modify agent behavior without code changes.","intents":["I want to version and test prompts separately from my application code","I need to enable product teams to iterate on agent behavior without engineering involvement","I want to A/B test different prompt strategies and measure their impact on agent reliability"],"best_for":["product teams managing multiple agent variants with different prompting strategies","organizations with separate prompt engineering and software engineering roles","teams needing audit trails of prompt changes and their correlation with agent behavior changes"],"limitations":["Requires external prompt storage or configuration management system (not built-in)","No built-in analytics for measuring prompt effectiveness — requires integration with observability tools","Prompt versioning adds complexity to agent state management if not carefully designed"],"requires":["Configuration management system (environment variables, config files, or external service)","Version control for prompt artifacts","Logging/observability system to correlate prompt versions with agent outcomes"],"input_types":["prompt templates with variable placeholders","system instructions and role definitions","dynamic context to be injected at runtime"],"output_types":["versioned prompt artifacts","rendered prompts with injected context","prompt change audit logs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_10","uri":"capability://automation.workflow.trigger.from.anywhere.event.driven.invocation","name":"trigger-from-anywhere-event-driven-invocation","description":"Enables agents to be triggered from any event source (webhooks, message queues, scheduled jobs, user actions) through a unified invocation interface, rather than being tightly coupled to specific trigger mechanisms. Implements Factor 11 by decoupling agent invocation from trigger sources, allowing the same agent to be triggered by multiple sources without modification. Uses an event adapter pattern to normalize different trigger types into a common agent invocation format.","intents":["I want to trigger my agent from multiple sources (webhooks, queues, schedules) without code changes","I need to decouple my agent from specific trigger mechanisms for flexibility","I want to add new trigger sources without modifying agent code"],"best_for":["systems with multiple trigger sources that need to invoke the same agent","teams building event-driven architectures with agents","applications requiring flexible agent invocation patterns"],"limitations":["Requires event adapter layer — adds complexity and potential latency","Trigger normalization may lose source-specific context","Debugging becomes more complex with multiple trigger paths"],"requires":["Event adapter/normalization layer","Unified agent invocation interface","Support for multiple event sources (webhooks, queues, etc.)"],"input_types":["events from various sources (webhooks, queue messages, scheduled triggers)","event normalization rules"],"output_types":["normalized agent invocation requests","agent execution results","trigger source metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_11","uri":"capability://planning.reasoning.stateless.reducer.agent.execution.model","name":"stateless-reducer-agent-execution-model","description":"Implements agents as pure, stateless reducers that take a state snapshot and an action, produce a new state snapshot, and have no side effects outside of state mutation. Implements Factor 12 by treating agent execution as a functional transformation where each step is deterministic and reproducible, enabling perfect replay, time-travel debugging, and easy testing. Uses an immutable state model where every action produces a new state snapshot rather than mutating state in place.","intents":["I want to replay agent execution from any point in time for debugging","I need to test agent behavior deterministically without side effects","I want to implement time-travel debugging for agent execution"],"best_for":["teams requiring perfect auditability and replay capability for agent execution","systems where debugging agent behavior is critical","applications needing deterministic, reproducible agent execution"],"limitations":["Stateless reducer model requires careful design to avoid side effects","Immutable state can lead to performance issues with large state objects","Requires discipline from developers to maintain pure reducer semantics"],"requires":["Immutable state representation","Pure reducer function implementation","State snapshot storage for replay"],"input_types":["current state snapshot","agent action"],"output_types":["new state snapshot","state transition log"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_12","uri":"capability://memory.knowledge.context.prefetching.and.preloading","name":"context-prefetching-and-preloading","description":"Proactively fetches and preloads context data before agent execution begins, reducing latency and ensuring critical information is available without requiring the agent to fetch it during execution. Implements Factor 13 (appendix) by identifying context dependencies upfront and loading them in parallel before the agent starts reasoning, rather than having the agent fetch context on-demand. Uses dependency analysis to determine what context is needed and prefetch strategies to optimize loading.","intents":["I want to reduce agent execution latency by prefetching context before execution starts","I need to ensure critical context is available without requiring the agent to fetch it","I want to optimize context loading by parallelizing prefetch operations"],"best_for":["latency-sensitive applications where agent response time is critical","systems with expensive context fetching operations (database queries, API calls)","applications with predictable context dependencies"],"limitations":["Requires upfront analysis of context dependencies — not all dependencies are predictable","Prefetching unused context wastes resources and increases costs","Context may become stale between prefetch time and agent execution time"],"requires":["Context dependency analysis mechanism","Parallel prefetch execution capability","Context caching and invalidation strategy"],"input_types":["agent task/request","context dependency rules","context sources (databases, APIs, etc.)"],"output_types":["prefetched context data","context availability status","prefetch timing metrics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_13","uri":"capability://code.generation.editing.agent.template.and.scaffolding.generation","name":"agent-template-and-scaffolding-generation","description":"Provides code generation and scaffolding tools that generate boilerplate agent implementations from high-level specifications, reducing the effort required to implement agents that follow 12-Factor principles. Includes tools like 'walkthroughgen' that analyze existing agent implementations and generate documentation, tests, or new agent variants. Uses code analysis and template-based generation to create consistent, production-ready agent code.","intents":["I want to quickly scaffold a new agent that follows 12-Factor principles","I need to generate documentation and tests for my agent implementation","I want to create agent variants without duplicating code"],"best_for":["teams building multiple agents and wanting consistency across implementations","developers new to 12-Factor agents who need guidance on structure","organizations wanting to enforce architectural patterns across agent implementations"],"limitations":["Generated code may require customization for domain-specific logic","Scaffolding tools assume specific architectural patterns — may not fit all use cases","Generated code quality depends on template quality"],"requires":["Code generation framework","Agent specification format or templates","Analysis tools (walkthroughgen or similar)"],"input_types":["agent specifications or existing agent code","scaffolding templates"],"output_types":["generated agent code","generated tests","generated documentation"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_14","uri":"capability://code.generation.editing.agent.testing.and.validation.framework","name":"agent-testing-and-validation-framework","description":"Provides testing infrastructure for agents including unit tests, integration tests, and validation of agent behavior against expected outcomes, with support for deterministic replay and scenario-based testing. Enables testing of agent decision-making, tool call validation, and state transitions in isolation without requiring live LLM calls. Uses snapshot testing and scenario-based approaches to validate agent behavior.","intents":["I want to test my agent behavior without making expensive LLM API calls","I need to validate that my agent makes correct decisions in specific scenarios","I want to catch regressions in agent behavior when I update prompts or logic"],"best_for":["teams building production agents that need high confidence in behavior","systems where agent reliability is critical","organizations wanting to catch regressions before deployment"],"limitations":["Testing without live LLM calls requires mocking/stubbing, which may not capture real LLM behavior","Scenario-based testing requires upfront investment in test case design","No built-in mechanism to test against multiple LLM models"],"requires":["Test framework (Jest, pytest, etc.)","LLM mocking/stubbing capability","Test scenario definitions"],"input_types":["agent code","test scenarios","expected outcomes"],"output_types":["test results","coverage reports","behavior validation reports"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_15","uri":"capability://tool.use.integration.baml.based.structured.output.integration","name":"baml-based-structured-output-integration","description":"Integrates with BAML (Boundary Augmented Markup Language) for defining and validating structured outputs from LLMs, providing a domain-specific language for specifying tool schemas, output formats, and validation rules. BAML integration enables type-safe tool definitions and structured output validation without requiring manual JSON Schema definition. Uses BAML's parsing and validation capabilities to ensure LLM outputs conform to expected schemas.","intents":["I want to define tool schemas and structured outputs in a more readable format than JSON Schema","I need type-safe tool definitions that work across TypeScript and Python","I want to validate LLM outputs against schemas without manual validation code"],"best_for":["teams using BAML for structured output definition","polyglot teams needing consistent schema definitions across languages","systems requiring type-safe tool definitions"],"limitations":["Requires learning BAML syntax — adds learning curve","BAML ecosystem is smaller than JSON Schema — fewer tools and integrations","BAML validation adds latency (~50-100ms per validation)"],"requires":["BAML compiler and runtime","BAML schema definitions","TypeScript or Python SDK"],"input_types":["BAML schema definitions","LLM structured outputs"],"output_types":["validated structured outputs","type-safe objects","validation error reports"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_16","uri":"capability://automation.workflow.thread.and.event.management.system","name":"thread-and-event-management-system","description":"Manages agent execution threads and events, tracking the sequence of agent actions, tool calls, and state transitions in a structured event log. Provides mechanisms to query execution history, replay events, and correlate events across multiple agents or execution threads. Uses an event sourcing pattern where every significant action is recorded as an immutable event.","intents":["I want to track the complete execution history of my agent for auditing","I need to correlate events across multiple agents or execution threads","I want to replay agent execution from any point in the event log"],"best_for":["mission-critical agents requiring complete audit trails","systems with multi-agent workflows needing event correlation","applications requiring compliance with regulatory audit requirements"],"limitations":["Event log storage can grow large for long-running agents — requires cleanup/archival strategy","Event sourcing adds complexity to state management","Querying large event logs can be slow without proper indexing"],"requires":["Event storage system (database, event log, etc.)","Event schema and serialization format","Event query and replay mechanisms"],"input_types":["agent actions and state transitions","tool call invocations and results","external system responses"],"output_types":["event log entries","execution history queries","event replay capability"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_2","uri":"capability://memory.knowledge.context.window.aware.memory.management","name":"context-window-aware-memory-management","description":"Implements intelligent context window budgeting and management to prevent token overflow and ensure critical information remains available to the LLM throughout agent execution. Implements Factor 3 by providing explicit control over what gets included in the context window, with strategies for prioritizing recent events, important facts, and error information while dropping less critical context when space is constrained. Uses a sliding window or priority-based eviction strategy rather than naive context truncation.","intents":["I need to ensure my agent doesn't lose critical context due to token limits","I want to prioritize recent events and errors in the context window over older history","I need to measure and optimize my agent's context window usage to reduce costs"],"best_for":["teams building long-running agents that accumulate significant conversation history","applications with strict token budgets or cost constraints","systems requiring deterministic context inclusion (e.g., regulatory compliance)"],"limitations":["Requires explicit prioritization logic — no universal 'best' strategy for all domains","Context eviction can cause loss of important historical information if priority rules are misconfigured","Adds ~100-300ms overhead per context window calculation depending on history size"],"requires":["Token counting library compatible with target LLM (tiktoken for OpenAI, etc.)","Clear definition of context priority rules for the domain","Mechanism to track and measure context window usage"],"input_types":["conversation history (messages, events)","system context (facts, rules, state)","error logs and diagnostic information","context priority configuration"],"output_types":["token-budgeted context window","context inclusion/exclusion decisions with reasoning","context window utilization metrics"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_3","uri":"capability://tool.use.integration.structured.output.tool.definition.framework","name":"structured-output-tool-definition-framework","description":"Defines tools as structured output schemas rather than arbitrary functions, ensuring every tool has a well-defined input/output contract that both the LLM and deterministic code can understand. Implements Factor 4 by treating tool definitions as data structures with explicit type information, validation rules, and documentation, enabling the system to generate tool descriptions for the LLM and validate tool responses before execution. Tools are defined declaratively (likely via JSON Schema or similar) rather than as imperative function signatures.","intents":["I want to ensure every tool my agent can call has a clear, validated input/output contract","I need to generate accurate tool descriptions for the LLM without manual documentation","I want to validate tool responses before they affect system state"],"best_for":["teams building agents with large tool sets (10+ tools) requiring consistency","systems where tool contracts must be validated and auditable","organizations needing automatic tool documentation generation"],"limitations":["Requires upfront investment in tool schema definition — slower initial development","Schema-based approach may be overly rigid for tools with highly dynamic signatures","Tool response validation adds latency (~50-150ms per tool call)"],"requires":["JSON Schema or equivalent structured schema language","Tool registry or catalog system","Response validation library"],"input_types":["tool schema definitions (JSON Schema format)","tool implementation code","tool response data"],"output_types":["validated tool definitions","LLM-readable tool descriptions","validated tool responses","schema validation errors"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_4","uri":"capability://automation.workflow.unified.execution.and.business.state.management","name":"unified-execution-and-business-state-management","description":"Unifies the agent's execution state (what step it's on, what tools it's called, what errors occurred) with the application's business state (user data, domain objects, transaction state) into a single, consistent state representation. Implements Factor 5 by ensuring the agent's internal reasoning state and the application's persistent state are always synchronized, preventing divergence where the agent thinks it succeeded but the business operation failed. Uses a single state reducer pattern where each agent action produces a new state snapshot.","intents":["I need to ensure my agent's internal state always matches my application's business state","I want to replay agent execution from any point in time for debugging or auditing","I need to prevent race conditions where agent state and business state diverge"],"best_for":["mission-critical agents handling financial transactions or data mutations","systems requiring complete audit trails of agent decisions and their business impact","teams needing to debug agent behavior by replaying execution with the same state"],"limitations":["Requires careful state schema design — poor design leads to state explosion and performance issues","State synchronization adds latency and complexity to every agent action","Difficult to implement with legacy systems that don't expose state as immutable snapshots"],"requires":["Immutable state representation (likely TypeScript/JavaScript objects or similar)","Event sourcing or similar pattern for state history","Transactional guarantees for state mutations"],"input_types":["agent actions (tool calls, decisions)","business operation results","external system responses"],"output_types":["unified state snapshots","state transition logs","state consistency validation reports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_5","uri":"capability://automation.workflow.agent.lifecycle.control.with.pause.resume","name":"agent-lifecycle-control-with-pause-resume","description":"Provides simple APIs to launch, pause, resume, and cancel agent execution without losing state or context, enabling long-running agents to be interrupted for human review or system maintenance. Implements Factor 6 by treating agent execution as a pausable process with explicit state checkpoints, allowing the agent to be suspended mid-execution, inspected, and resumed from the exact point of interruption. Uses a state-machine approach where pause/resume transitions are explicit and validated.","intents":["I need to pause my agent for human review before it takes a critical action","I want to resume an agent after a system failure without losing progress","I need to cancel an agent execution and clean up its resources gracefully"],"best_for":["agents handling high-stakes decisions (financial, legal, operational) requiring human approval","systems with unreliable infrastructure where agents may be interrupted","applications needing to implement human-in-the-loop workflows"],"limitations":["Requires explicit state persistence — no built-in storage provided","Pause/resume semantics must be carefully defined for each agent type","Long-paused agents may have stale context when resumed (e.g., external data changes)"],"requires":["Persistent state storage (database, file system, etc.)","Mechanism to track agent execution state and checkpoints","Clear definition of pausable/resumable agent states"],"input_types":["agent execution ID","pause/resume/cancel commands","human review decisions"],"output_types":["agent execution status","current agent state snapshot","execution history and logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_6","uri":"capability://tool.use.integration.human.contact.via.tool.calls","name":"human-contact-via-tool-calls","description":"Enables agents to contact humans (request approval, ask clarifying questions, escalate decisions) by treating human contact as a tool call rather than a special case, maintaining consistency with the tool-call abstraction. Implements Factor 7 by allowing the LLM to decide when human input is needed and what information to request, with the human response being treated as a tool result that feeds back into the agent's reasoning loop. Human contact is asynchronous and non-blocking, allowing the agent to be paused while awaiting human response.","intents":["I want my agent to ask for human approval before taking critical actions","I need my agent to request clarification when it's uncertain about user intent","I want to escalate decisions to humans when the agent's confidence is below a threshold"],"best_for":["agents handling high-stakes decisions requiring human oversight","systems where agents need to gather additional information from users mid-execution","applications implementing human-in-the-loop approval workflows"],"limitations":["Requires human availability and response time — agent execution is blocked until human responds","No built-in timeout or escalation if human doesn't respond within SLA","Treating human contact as a tool call may feel unnatural for some use cases"],"requires":["Human notification system (email, Slack, in-app notification, etc.)","Mechanism to collect and validate human responses","Timeout and escalation policies for unresponded human requests"],"input_types":["agent reasoning indicating need for human input","context and information to present to human","human response/decision"],"output_types":["human contact request with context","human response/decision","agent execution resumption with human input"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_7","uri":"capability://planning.reasoning.explicit.control.flow.ownership","name":"explicit-control-flow-ownership","description":"Provides explicit, deterministic control flow logic that the agent cannot override, ensuring critical business logic and safety constraints are enforced regardless of LLM reasoning. Implements Factor 8 by separating the agent's decision-making (what tool to call) from the application's control flow (whether that tool call is allowed, what happens next), with the control flow implemented in deterministic code rather than left to the LLM. Uses a state machine or workflow engine to define valid state transitions and enforce business rules.","intents":["I need to ensure my agent can't bypass critical business logic or safety constraints","I want to define valid agent workflows and prevent invalid state transitions","I need to enforce regulatory or compliance requirements that the LLM shouldn't override"],"best_for":["regulated industries (finance, healthcare, legal) where compliance is non-negotiable","systems with complex business logic that must be enforced regardless of agent reasoning","applications where agent autonomy must be bounded by explicit rules"],"limitations":["Requires upfront design of valid control flows — adds development overhead","Overly restrictive control flow can prevent agents from handling edge cases","Control flow changes require code updates, not just prompt changes"],"requires":["State machine or workflow engine","Clear definition of valid state transitions and business rules","Mechanism to enforce control flow constraints at runtime"],"input_types":["agent decisions (tool calls)","current execution state","business rules and constraints"],"output_types":["validated state transitions","control flow enforcement decisions","constraint violation reports"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_8","uri":"capability://memory.knowledge.compact.error.representation.for.context.window","name":"compact-error-representation-for-context-window","description":"Compacts error information into a concise, context-window-efficient format that provides the agent with actionable debugging information without consuming excessive tokens. Implements Factor 9 by extracting the most relevant error details (error type, root cause, suggested remediation) and presenting them in a structured format that the LLM can efficiently process, rather than including full stack traces or verbose error messages. Uses error categorization and templating to ensure consistency.","intents":["I want my agent to learn from errors without wasting context window on verbose error messages","I need to provide actionable error information that helps the agent recover or retry","I want to reduce token usage by compacting error representations"],"best_for":["long-running agents that accumulate many errors and need to stay within token budgets","systems where error recovery is critical and agents must learn from failures","applications with strict context window constraints"],"limitations":["Requires careful error categorization — poorly categorized errors lose important context","Compacted errors may not contain enough information for human debugging","Adds complexity to error handling logic"],"requires":["Error categorization and classification system","Error templating or formatting rules","Mechanism to extract actionable error information"],"input_types":["raw errors (exceptions, API responses, validation failures)","error context (what operation was being attempted)"],"output_types":["compacted error representations","structured error information (type, cause, remediation)","error categorization"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-humanlayer--12-factor-agents__cap_9","uri":"capability://planning.reasoning.micro.agent.decomposition.and.composition","name":"micro-agent-decomposition-and-composition","description":"Decomposes complex agent tasks into small, focused agents with single responsibilities that can be composed together, rather than building monolithic agents that handle multiple concerns. Implements Factor 10 by establishing clear boundaries between agent responsibilities, with each agent handling a specific domain or task type, and providing composition mechanisms to orchestrate multiple agents. Uses a DAG (directed acyclic graph) or similar pattern to define agent dependencies and execution order.","intents":["I want to break down complex agent tasks into smaller, testable units","I need to reuse agent logic across multiple workflows","I want to improve agent reliability by reducing the scope of each agent's responsibility"],"best_for":["teams building complex multi-step workflows that benefit from decomposition","systems where different agent types have different reliability/cost tradeoffs","applications needing to test and iterate on agent components independently"],"limitations":["Decomposition adds orchestration complexity — requires coordination between agents","Context passing between agents can lose important information","Debugging multi-agent workflows is more complex than single-agent systems"],"requires":["Agent composition/orchestration framework","Clear definition of agent responsibilities and boundaries","Mechanism to pass context between agents"],"input_types":["task decomposition specification","agent definitions","context to be passed between agents"],"output_types":["decomposed agent workflows","agent composition DAG","orchestration execution logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["TypeScript or Python runtime","JSON Schema or equivalent structured output format support","LLM provider with function calling or structured output API (OpenAI, Anthropic, etc.)","Configuration management system (environment variables, config files, or external service)","Version control for prompt artifacts","Logging/observability system to correlate prompt versions with agent outcomes","Event adapter/normalization layer","Unified agent invocation interface","Support for multiple event sources (webhooks, queues, etc.)","Immutable state representation"],"failure_modes":["Requires explicit schema definition for every tool, adding upfront design overhead","Schema validation adds latency per tool call (~50-200ms depending on complexity)","Does not handle tools with highly dynamic or user-defined signatures without schema regeneration","Requires external prompt storage or configuration management system (not built-in)","No built-in analytics for measuring prompt effectiveness — requires integration with observability tools","Prompt versioning adds complexity to agent state management if not carefully designed","Requires event adapter layer — adds complexity and potential latency","Trigger normalization may lose source-specific context","Debugging becomes more complex with multiple trigger paths","Stateless reducer model requires careful design to avoid side effects","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7214037827123441,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:50.673Z","last_commit":"2025-09-21T14:37:40Z"},"community":{"stars":19623,"forks":1487,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=humanlayer--12-factor-agents","compare_url":"https://unfragile.ai/compare?artifact=humanlayer--12-factor-agents"}},"signature":"YngWNoAN48Z4Ra+3jmoutzN052g60s4IEr15ChbW0pTlQS9TJKMoXAswcrnsiRwRqQIyUOmSXUSsRPgZc7VZCQ==","signedAt":"2026-06-20T07:44:47.408Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/humanlayer--12-factor-agents","artifact":"https://unfragile.ai/humanlayer--12-factor-agents","verify":"https://unfragile.ai/api/v1/verify?slug=humanlayer--12-factor-agents","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}