{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-46731256","slug":"build-agents-via-yaml-with-prolog-validation-and-1","name":"Build agents via YAML with Prolog validation and 110 built-in tools","type":"agent","url":"https://fabceolin.github.io/the_edge_agent/index.html","page_url":"https://unfragile.ai/build-agents-via-yaml-with-prolog-validation-and-1","categories":["ai-agents","deployment-infra"],"tags":["hackernews","show-hn"],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-46731256__cap_0","uri":"capability://automation.workflow.yaml.based.agent.configuration.with.declarative.syntax","name":"yaml-based agent configuration with declarative syntax","description":"Enables agent definition through YAML configuration files rather than imperative code, parsing the YAML structure into an internal agent representation that maps to tool registries and execution pipelines. The declarative approach abstracts away boilerplate orchestration logic, allowing non-developers to compose multi-step workflows by declaring agent goals, tools, and execution constraints in human-readable YAML syntax.","intents":["Define multi-step agent workflows without writing Python or JavaScript","Version control agent configurations as code artifacts","Enable non-technical stakeholders to modify agent behavior without code changes","Rapidly prototype agent behavior by editing configuration files"],"best_for":["teams building internal automation tools with non-technical operators","organizations seeking configuration-driven agent deployment","rapid prototyping scenarios where code iteration is expensive"],"limitations":["YAML syntax limits expressiveness compared to imperative languages — complex conditional logic requires workarounds","No built-in IDE support for schema validation or autocomplete in standard editors","Debugging YAML-defined agents requires parsing error messages back to configuration lines"],"requires":["YAML parser compatible with the agent framework","Valid YAML syntax (whitespace-sensitive, prone to indentation errors)","Understanding of agent framework's YAML schema and reserved keywords"],"input_types":["YAML files","YAML strings"],"output_types":["agent instance","execution plan"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_1","uri":"capability://safety.moderation.prolog.based.agent.validation.and.constraint.checking","name":"prolog-based agent validation and constraint checking","description":"Integrates Prolog logic programming to validate agent configurations before execution, checking for logical consistency, constraint satisfaction, and goal reachability. The validation engine translates YAML agent definitions into Prolog predicates, then queries the Prolog engine to detect configuration errors (unreachable goals, circular dependencies, missing tool bindings) before the agent runs, preventing runtime failures.","intents":["Catch configuration errors before agent deployment","Validate that agent goals are logically achievable with available tools","Detect circular dependencies or infinite loops in agent workflows","Ensure constraint satisfaction (e.g., tool prerequisites, resource limits)"],"best_for":["teams deploying agents in production where validation prevents costly failures","systems requiring formal verification of agent behavior before execution","organizations with strict compliance requirements for automated decision-making"],"limitations":["Prolog validation adds startup latency — complex agent graphs may require seconds to validate","Prolog's declarative nature makes it difficult to express temporal constraints or stateful validations","Limited to logical consistency checks — cannot validate semantic correctness or real-world feasibility"],"requires":["Prolog engine (SWI-Prolog or compatible)","YAML agent configuration in valid format","Understanding of Prolog syntax for custom constraint rules (optional)"],"input_types":["YAML agent configuration","Prolog constraint rules"],"output_types":["validation report","constraint satisfaction result","error list"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_10","uri":"capability://automation.workflow.agent.performance.monitoring.and.metrics.collection","name":"agent performance monitoring and metrics collection","description":"Collects metrics on agent execution performance (latency per step, tool invocation counts, success rates, error rates) and exposes them for monitoring and alerting. The metrics system tracks execution time, tool usage patterns, and failure modes, enabling operators to identify performance bottlenecks and detect anomalies in agent behavior.","intents":["Monitor agent execution latency and identify slow steps","Track tool usage patterns to optimize tool selection","Detect anomalies in agent behavior (unusual error rates, tool combinations)","Measure agent success rates and identify failure modes"],"best_for":["production systems requiring operational visibility into agent performance","teams optimizing agent efficiency and cost","systems with SLAs requiring performance monitoring and alerting"],"limitations":["Metrics collection adds overhead (5-10% latency increase)","Metrics storage can grow large for high-volume agent executions","Anomaly detection requires baseline data and tuning","No built-in integration with monitoring platforms — requires custom exporters"],"requires":["Metrics collection enabled in agent configuration","Time source for latency measurement","Metrics storage or export mechanism (Prometheus, CloudWatch, etc.)"],"input_types":["execution events","tool invocations","error events"],"output_types":["performance metrics","latency histogram","error rate","tool usage statistics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_2","uri":"capability://tool.use.integration.110.built.in.tool.integration.with.unified.calling.interface","name":"110 built-in tool integration with unified calling interface","description":"Provides a pre-integrated library of 110 tools (APIs, functions, external services) accessible through a unified function-calling interface. Tools are registered in a central registry with standardized schemas (name, description, parameters, return type), allowing agents to discover and invoke tools via a single abstraction layer without custom integration code for each tool.","intents":["Access common utilities (math, string manipulation, file I/O) without custom implementation","Integrate with external APIs (weather, news, search) without writing adapter code","Compose multi-tool workflows where agent selects appropriate tools for each step","Extend agent capabilities without modifying core framework code"],"best_for":["developers building general-purpose agents that need broad tool coverage","teams avoiding custom tool integration overhead","rapid prototyping scenarios where pre-built tools accelerate development"],"limitations":["110 tools may not cover specialized domain-specific operations — custom tools still required for niche use cases","Tool quality and maintenance varies — some tools may be outdated or have limited documentation","Tool invocation latency depends on underlying implementation (network calls for external APIs add 100-5000ms per call)","No built-in rate limiting or quota management for external API tools"],"requires":["API keys or credentials for external service tools (weather, search, etc.)","Network connectivity for tools that call remote APIs","Tool registry initialized with all 110 tools loaded"],"input_types":["tool name","tool parameters (JSON-serializable)"],"output_types":["tool result","structured data","error message"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_3","uri":"capability://planning.reasoning.agent.execution.orchestration.with.step.by.step.planning","name":"agent execution orchestration with step-by-step planning","description":"Orchestrates agent execution by decomposing high-level goals into discrete steps, planning tool invocations, and managing state across execution steps. The orchestrator maintains an execution context (current goal, available tools, previous results) and iteratively selects the next tool to invoke based on agent reasoning, handling tool outputs and updating state until the goal is achieved or a termination condition is met.","intents":["Execute multi-step workflows where each step depends on previous results","Manage agent state and context across tool invocations","Handle tool failures and retry logic transparently","Provide visibility into agent decision-making process"],"best_for":["complex workflows requiring sequential tool composition","scenarios where agent reasoning must be auditable or explainable","systems needing fine-grained control over execution flow"],"limitations":["Step-by-step planning adds latency — each step requires reasoning overhead (100-500ms per step typical)","No built-in backtracking — if a tool choice leads to a dead end, agent cannot automatically explore alternative paths","Limited to sequential execution — parallel tool invocations not supported","Execution context size grows with each step — may hit token limits for very long workflows"],"requires":["LLM or reasoning engine to select next tool at each step","Tool registry with all available tools","Execution context storage (in-memory or external state store)"],"input_types":["goal statement","execution context","tool results from previous steps"],"output_types":["final result","execution trace","step-by-step log"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_4","uri":"capability://planning.reasoning.agent.goal.decomposition.and.subgoal.generation","name":"agent goal decomposition and subgoal generation","description":"Automatically breaks down high-level agent goals into smaller, achievable subgoals by analyzing the goal statement and available tools. The decomposition engine uses heuristics or reasoning to identify prerequisite steps, dependencies between subgoals, and optimal ordering, generating an execution plan that guides the agent through sequential subgoal achievement.","intents":["Convert vague user requests into concrete, executable subgoals","Identify prerequisite steps needed before main goal can be achieved","Optimize execution order to minimize tool invocations or latency","Handle complex goals that require multiple tools in sequence"],"best_for":["agents handling open-ended user requests requiring multi-step reasoning","systems where goal clarity is critical before execution begins","scenarios where execution order significantly impacts performance or cost"],"limitations":["Decomposition quality depends on goal clarity — ambiguous goals produce suboptimal plans","No guarantee of optimal decomposition — heuristic-based approach may miss better execution paths","Decomposition itself requires reasoning overhead (200-1000ms for complex goals)","Cannot handle dynamic goals that change based on intermediate results"],"requires":["Clear goal statement with sufficient context","Tool registry with descriptions enabling relevance matching","Reasoning engine (LLM or symbolic planner)"],"input_types":["goal statement","available tools list"],"output_types":["subgoal list","execution plan","dependency graph"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_5","uri":"capability://tool.use.integration.tool.parameter.binding.and.schema.validation","name":"tool parameter binding and schema validation","description":"Validates and binds parameters to tool invocations by matching agent-generated parameters against tool schemas (expected types, required fields, constraints). The binding engine performs type coercion, validates parameter values against constraints, and generates clear error messages when parameters are invalid, preventing malformed tool calls from reaching the underlying tool.","intents":["Ensure tool parameters match expected types and formats before invocation","Automatically coerce parameter types when safe (string to int, etc.)","Validate parameter values against constraints (ranges, allowed values, regex patterns)","Provide clear error messages when parameter binding fails"],"best_for":["systems requiring strict parameter validation before tool execution","scenarios where tool failures due to invalid parameters are costly","teams needing detailed error reporting for debugging agent behavior"],"limitations":["Schema validation adds latency per tool invocation (10-50ms typical)","Type coercion heuristics may fail for complex types or ambiguous conversions","No built-in support for custom validation logic — requires extending framework","Error messages may be cryptic for nested or complex parameter structures"],"requires":["Tool schemas with parameter definitions (name, type, constraints)","Parameter values from agent reasoning","Type system compatible with tool framework"],"input_types":["tool name","raw parameters (any JSON-serializable type)"],"output_types":["validated parameters","validation error","type coercion result"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_6","uri":"capability://automation.workflow.agent.execution.tracing.and.debugging.output","name":"agent execution tracing and debugging output","description":"Records detailed execution traces showing each step of agent reasoning, tool invocations, parameters, results, and state changes. The tracing system captures execution metadata (timestamps, latency, tool selection rationale) and outputs structured logs or interactive visualizations, enabling developers to understand agent behavior and diagnose failures without modifying code.","intents":["Debug agent behavior by inspecting step-by-step execution","Understand why agent selected specific tools at each step","Measure execution latency and identify performance bottlenecks","Audit agent decisions for compliance or transparency requirements"],"best_for":["development and testing phases where visibility into agent behavior is critical","production systems requiring audit trails of automated decisions","teams debugging complex multi-step workflows"],"limitations":["Detailed tracing adds overhead — 5-20% latency increase depending on trace verbosity","Trace output can be very large for long-running agents — requires storage and log management","Tracing may expose sensitive information (API keys, user data) — requires careful log sanitization","Interactive debugging tools may not be available in all deployment environments"],"requires":["Tracing enabled in agent configuration","Log storage or output system (stdout, file, cloud logging)","Optional: visualization tool or dashboard for interactive debugging"],"input_types":["execution events","tool invocations","state snapshots"],"output_types":["execution trace","structured logs","visualization data"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_7","uri":"capability://safety.moderation.constraint.based.tool.selection.and.filtering","name":"constraint-based tool selection and filtering","description":"Filters available tools based on agent-defined constraints (resource limits, permission levels, execution context) before presenting them to the reasoning engine. The filtering system evaluates Prolog constraints against the current execution context, removing tools that violate constraints and ensuring only valid tools are considered for selection at each step.","intents":["Restrict tool access based on execution context (e.g., only read-only tools in safe mode)","Enforce resource limits (e.g., prevent expensive API calls when budget exhausted)","Implement permission-based tool access control","Prevent dangerous tool combinations or sequences"],"best_for":["multi-tenant systems requiring per-user or per-context tool restrictions","safety-critical applications where tool access must be tightly controlled","cost-sensitive deployments where expensive tools must be rationed"],"limitations":["Constraint evaluation adds latency per step (50-200ms for complex constraints)","Overly restrictive constraints may make goals unreachable — requires careful constraint design","No built-in support for dynamic constraint updates during execution","Constraint violations may not provide clear guidance on how to modify constraints"],"requires":["Prolog constraint rules defined in agent configuration","Execution context with relevant state (budget, permissions, mode)","Tool registry with constraint metadata"],"input_types":["execution context","constraint rules","available tools"],"output_types":["filtered tool list","constraint violation report"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_8","uri":"capability://automation.workflow.multi.agent.coordination.and.message.passing","name":"multi-agent coordination and message passing","description":"Enables multiple agents to coordinate by passing messages and sharing execution context. Agents can invoke other agents as tools, passing goals and receiving results, with a message queue or event bus managing communication between agents. The coordination layer handles agent discovery, message routing, and context propagation across agent boundaries.","intents":["Decompose complex problems across multiple specialized agents","Enable agents to delegate subtasks to other agents","Share execution context and results between agents","Implement hierarchical agent structures (parent agents coordinating child agents)"],"best_for":["large-scale systems where problem decomposition across agents improves modularity","scenarios with specialized agents (e.g., research agent, planning agent, execution agent)","teams building multi-agent systems with clear separation of concerns"],"limitations":["Inter-agent communication adds latency — message passing and context propagation overhead (100-500ms per agent handoff)","No built-in deadlock detection — circular agent dependencies can cause infinite loops","Context propagation may hit token limits when passing large execution traces between agents","Debugging multi-agent systems is significantly more complex than single-agent systems"],"requires":["Message queue or event bus for inter-agent communication","Agent registry for agent discovery","Shared execution context storage or serialization mechanism"],"input_types":["agent message","goal for delegated agent","execution context"],"output_types":["agent result","message acknowledgment","coordination status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-46731256__cap_9","uri":"capability://automation.workflow.agent.configuration.versioning.and.rollback","name":"agent configuration versioning and rollback","description":"Manages versions of agent YAML configurations, enabling rollback to previous versions and comparison between configurations. The versioning system tracks configuration changes, stores historical versions, and provides diff tools to understand what changed between versions, supporting safe configuration updates and quick recovery from problematic deployments.","intents":["Track changes to agent configurations over time","Rollback to previous agent versions if new configuration causes problems","Compare configurations to understand what changed between versions","Maintain audit trail of agent configuration changes"],"best_for":["production deployments where configuration rollback is critical for reliability","teams using configuration-driven agents with frequent updates","regulated environments requiring audit trails of configuration changes"],"limitations":["Versioning adds storage overhead — each version requires full configuration copy","No built-in conflict resolution for concurrent configuration updates","Rollback is instantaneous but may not address data state changes made by previous agent version","Version comparison tools may be limited for complex nested YAML structures"],"requires":["Version control system (Git, etc.) or built-in versioning storage","YAML diff tool for comparing configurations","Deployment mechanism that can switch between versions"],"input_types":["YAML configuration","version identifier"],"output_types":["configuration version","diff report","version history"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["YAML parser compatible with the agent framework","Valid YAML syntax (whitespace-sensitive, prone to indentation errors)","Understanding of agent framework's YAML schema and reserved keywords","Prolog engine (SWI-Prolog or compatible)","YAML agent configuration in valid format","Understanding of Prolog syntax for custom constraint rules (optional)","Metrics collection enabled in agent configuration","Time source for latency measurement","Metrics storage or export mechanism (Prometheus, CloudWatch, etc.)","API keys or credentials for external service tools (weather, search, etc.)"],"failure_modes":["YAML syntax limits expressiveness compared to imperative languages — complex conditional logic requires workarounds","No built-in IDE support for schema validation or autocomplete in standard editors","Debugging YAML-defined agents requires parsing error messages back to configuration lines","Prolog validation adds startup latency — complex agent graphs may require seconds to validate","Prolog's declarative nature makes it difficult to express temporal constraints or stateful validations","Limited to logical consistency checks — cannot validate semantic correctness or real-world feasibility","Metrics collection adds overhead (5-10% latency increase)","Metrics storage can grow large for high-volume agent executions","Anomaly detection requires baseline data and tuning","No built-in integration with monitoring platforms — requires custom exporters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36,"quality":0.32,"ecosystem":0.31,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.326Z","last_scraped_at":"2026-05-04T08:09:59.925Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=build-agents-via-yaml-with-prolog-validation-and-1","compare_url":"https://unfragile.ai/compare?artifact=build-agents-via-yaml-with-prolog-validation-and-1"}},"signature":"WLZAAeX0NmQB3g/EtR0avTkKlfyRbOzfKZaGjg1RMrYK62WgWxu+Cap30j9lFHEY7vSYGsYc8eFaUoYTbpZ+Dw==","signedAt":"2026-06-22T15:15:42.532Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/build-agents-via-yaml-with-prolog-validation-and-1","artifact":"https://unfragile.ai/build-agents-via-yaml-with-prolog-validation-and-1","verify":"https://unfragile.ai/api/v1/verify?slug=build-agents-via-yaml-with-prolog-validation-and-1","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"}}