{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-cognosys","slug":"cognosys","name":"Cognosys","type":"agent","url":"https://cognosys.ai","page_url":"https://unfragile.ai/cognosys","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-cognosys__cap_0","uri":"capability://planning.reasoning.autonomous.task.decomposition.and.execution","name":"autonomous task decomposition and execution","description":"Cognosys breaks down high-level user goals into discrete subtasks using an LLM-driven planning loop, then executes each subtask sequentially with state tracking across steps. The agent maintains a task queue and execution context, routing each subtask to appropriate tools (web search, code execution, file operations) based on inferred intent. This implements a goal-oriented agent loop similar to AutoGPT's task management, where the LLM both plans and decides when to delegate to external tools.","intents":["I want to automate a multi-step workflow without writing code","I need an agent to research, analyze, and summarize information across multiple sources","I want to delegate complex tasks that require planning and tool orchestration"],"best_for":["non-technical users automating business workflows","teams prototyping autonomous agent behavior without engineering overhead","researchers exploring agent-based task automation"],"limitations":["Task decomposition quality depends on LLM reasoning capability; complex multi-domain tasks may fail silently or produce suboptimal plans","No built-in rollback or error recovery — failed subtasks don't automatically trigger replanning","Execution context is ephemeral; long-running tasks (>30 min) may lose state if connection drops","Limited visibility into intermediate reasoning; debugging failed task chains requires manual inspection"],"requires":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","Active internet connection for LLM API calls and web access","API key or authentication for underlying LLM provider (likely OpenAI or similar)"],"input_types":["natural language task description","structured goal statements"],"output_types":["task execution logs","structured results from subtasks","web-scraped data","generated code or analysis"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cognosys__cap_1","uri":"capability://search.retrieval.web.search.and.information.retrieval.integration","name":"web search and information retrieval integration","description":"Cognosys integrates real-time web search capabilities into the agent loop, allowing tasks to fetch current information from the internet when needed. The agent decides autonomously whether a subtask requires web search, constructs search queries, parses results, and extracts relevant data. This is implemented as a tool within the agent's action space — the LLM can invoke web search as part of task execution, similar to how AutoGPT integrates Google Search API.","intents":["I need the agent to research current information without me providing sources","I want automated fact-checking or competitive analysis across multiple web sources","I need to gather real-time data (prices, news, availability) as part of a larger workflow"],"best_for":["market researchers automating competitive intelligence gathering","content creators needing real-time fact verification","business analysts automating data collection workflows"],"limitations":["Search result quality depends on query formulation; poorly phrased queries may return irrelevant results","No control over search ranking or filtering — agent receives raw search results and must parse them","Web scraping may be rate-limited or blocked by target sites; no built-in retry logic or proxy rotation","Parsing unstructured HTML is fragile; changes to website layouts can break data extraction"],"requires":["Active internet connection","Search API integration (likely Bing, Google, or similar)","API rate limits (typically 100-1000 queries/day depending on provider)"],"input_types":["natural language search intent","task context from parent workflow"],"output_types":["parsed web search results","extracted structured data from web pages","summarized information"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cognosys__cap_2","uri":"capability://code.generation.editing.code.generation.and.execution.in.sandboxed.environment","name":"code generation and execution in sandboxed environment","description":"Cognosys can generate code (Python, JavaScript, etc.) as part of task execution and run it in a sandboxed runtime environment. The agent decides when code execution is needed, generates appropriate code, executes it with timeout/resource limits, and captures output. This is implemented as a code execution tool within the agent's action space, similar to Jupyter kernel integration in AutoGPT, but running server-side rather than locally.","intents":["I want the agent to write and run code to process data or perform calculations","I need automated data transformation or analysis without writing the code myself","I want the agent to generate scripts that solve specific problems"],"best_for":["data analysts automating ETL and analysis workflows","developers prototyping code solutions without manual implementation","non-technical users leveraging code execution for complex tasks"],"limitations":["Sandboxed execution prevents access to local filesystem and system commands; limited to in-memory operations","Code generation quality depends on LLM capability; generated code may have bugs or inefficiencies","Execution timeout (likely 30-60 seconds) prevents long-running computations","No package installation at runtime — limited to pre-installed libraries","Memory limits may prevent processing large datasets"],"requires":["Server-side code execution runtime (Python, Node.js, or similar)","Sandboxing/containerization (Docker, WebAssembly, or VM-based isolation)","Resource limits and timeout enforcement"],"input_types":["natural language code request","data to be processed","task context"],"output_types":["generated code","execution results","error logs","processed data"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cognosys__cap_3","uri":"capability://automation.workflow.multi.step.workflow.orchestration.with.state.persistence","name":"multi-step workflow orchestration with state persistence","description":"Cognosys maintains execution state across multiple task steps, allowing workflows to reference previous results, build on intermediate outputs, and coordinate complex multi-stage processes. The agent tracks task history, variable bindings, and execution context, enabling later steps to depend on earlier results. This is implemented as a state machine or execution context manager that persists across the agent loop iterations.","intents":["I want to run a workflow where step 2 depends on results from step 1","I need to coordinate multiple tasks that share data or context","I want to automate a business process with multiple sequential stages"],"best_for":["business process automation teams building multi-stage workflows","data pipeline engineers automating ETL with dependencies","teams prototyping complex autonomous workflows"],"limitations":["State is ephemeral — no persistence across browser sessions or if connection drops","No built-in transaction semantics — partial failures don't trigger rollback","State size is limited by browser memory; large datasets in context may cause performance degradation","No audit trail or versioning of state changes; debugging requires manual inspection"],"requires":["Browser session persistence (cookies/local storage)","In-memory state management (likely Redux or similar)","No external database required for basic operation"],"input_types":["workflow definition (natural language or structured)","initial task parameters"],"output_types":["workflow execution log","final results","intermediate state snapshots"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cognosys__cap_4","uri":"capability://text.generation.language.natural.language.task.specification.and.refinement","name":"natural language task specification and refinement","description":"Cognosys accepts high-level goals expressed in natural language and iteratively refines them through conversation. The user describes what they want, the agent clarifies ambiguities, asks for missing context, and confirms understanding before execution. This is implemented as a conversational loop where the LLM acts as both task interpreter and clarification engine, similar to how AutoGPT handles user input.","intents":["I want to describe a task in plain English without learning a formal syntax","I need the agent to ask clarifying questions before starting work","I want to refine my task based on agent suggestions"],"best_for":["non-technical business users automating workflows","teams exploring agent capabilities without technical expertise","rapid prototyping of automation ideas"],"limitations":["Ambiguous or vague task descriptions may result in incorrect execution; no formal validation","Natural language interpretation is probabilistic — same input may produce different results across runs","No structured task schema — difficult to version or reproduce workflows","Clarification loop adds latency; users must wait for agent questions before execution"],"requires":["LLM with strong instruction-following capability","Conversational interface (chat UI)"],"input_types":["natural language task description","conversational refinement messages"],"output_types":["clarifying questions","task confirmation","execution plan summary"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cognosys__cap_5","uri":"capability://tool.use.integration.autonomous.tool.selection.and.invocation","name":"autonomous tool selection and invocation","description":"Cognosys maintains a registry of available tools (web search, code execution, file operations, etc.) and the agent autonomously decides which tools to invoke based on task requirements. The agent evaluates tool applicability, constructs appropriate inputs, invokes tools, and interprets results. This is implemented as a function-calling mechanism where the LLM selects from available tools and the runtime dispatches to appropriate handlers.","intents":["I want the agent to automatically choose the right tool for each subtask","I need the agent to combine multiple tools in a single workflow","I want to extend the agent's capabilities by adding new tools"],"best_for":["developers building extensible agent systems","teams wanting to customize agent capabilities for domain-specific tasks","organizations integrating agents with existing tool ecosystems"],"limitations":["Tool selection depends on LLM reasoning; poor descriptions or similar tool names may cause incorrect selection","No built-in tool chaining optimization — agent may invoke tools inefficiently","Tool integration requires API compatibility; custom tools need specific interface","No fallback mechanism if selected tool fails — agent must handle errors explicitly"],"requires":["Tool registry with metadata (name, description, parameters)","Function-calling API support from LLM provider","Tool execution runtime with error handling"],"input_types":["task description","available tool registry"],"output_types":["tool invocation logs","tool results","error messages"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cognosys__cap_6","uri":"capability://automation.workflow.execution.monitoring.and.error.recovery","name":"execution monitoring and error recovery","description":"Cognosys monitors task execution in real-time, detects failures, and attempts recovery through retry logic or alternative approaches. The agent observes tool outputs, identifies errors, and can modify its approach (e.g., reformulate a search query, try a different code approach). This is implemented as an observation loop where the agent evaluates success/failure and decides whether to retry, escalate, or abandon the task.","intents":["I want the agent to retry failed tasks automatically","I need visibility into what the agent is doing and why it's failing","I want the agent to adapt when its first approach doesn't work"],"best_for":["teams running long-running autonomous workflows requiring reliability","users wanting transparency into agent decision-making","systems requiring graceful degradation on partial failures"],"limitations":["Retry logic is heuristic-based; no guarantee of eventual success","Error detection depends on tool output format; some failures may go undetected","Recovery strategies are limited to retry and alternative approaches; no manual intervention hooks","Monitoring adds latency and computational overhead per step"],"requires":["Structured error reporting from tools","Retry budget/limits to prevent infinite loops","Logging infrastructure for execution visibility"],"input_types":["task execution context","tool outputs and errors"],"output_types":["execution logs with error details","recovery decisions","final success/failure status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cognosys__cap_7","uri":"capability://text.generation.language.execution.history.and.result.summarization","name":"execution history and result summarization","description":"Cognosys maintains a log of all executed tasks, tool invocations, and results, and can summarize execution history in natural language. Users can review what the agent did, why it made certain decisions, and what results were produced. This is implemented as an execution log with structured entries for each step, plus an LLM-based summarization capability to generate human-readable reports.","intents":["I want to review what the agent did and why","I need a summary of the workflow results for reporting","I want to audit the agent's decisions and actions"],"best_for":["compliance and audit teams requiring execution transparency","business users wanting to understand agent behavior","teams debugging failed workflows"],"limitations":["History is ephemeral — not persisted across sessions unless explicitly exported","Summarization quality depends on LLM; important details may be omitted","Large execution histories may be difficult to navigate or summarize","No structured query interface — searching history requires natural language"],"requires":["Execution log storage (in-memory or browser local storage)","LLM for summarization","UI for history browsing"],"input_types":["execution log entries"],"output_types":["structured execution history","natural language summary","detailed step-by-step logs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Web browser with modern JavaScript support (Chrome, Firefox, Safari, Edge)","Active internet connection for LLM API calls and web access","API key or authentication for underlying LLM provider (likely OpenAI or similar)","Active internet connection","Search API integration (likely Bing, Google, or similar)","API rate limits (typically 100-1000 queries/day depending on provider)","Server-side code execution runtime (Python, Node.js, or similar)","Sandboxing/containerization (Docker, WebAssembly, or VM-based isolation)","Resource limits and timeout enforcement","Browser session persistence (cookies/local storage)"],"failure_modes":["Task decomposition quality depends on LLM reasoning capability; complex multi-domain tasks may fail silently or produce suboptimal plans","No built-in rollback or error recovery — failed subtasks don't automatically trigger replanning","Execution context is ephemeral; long-running tasks (>30 min) may lose state if connection drops","Limited visibility into intermediate reasoning; debugging failed task chains requires manual inspection","Search result quality depends on query formulation; poorly phrased queries may return irrelevant results","No control over search ranking or filtering — agent receives raw search results and must parse them","Web scraping may be rate-limited or blocked by target sites; no built-in retry logic or proxy rotation","Parsing unstructured HTML is fragile; changes to website layouts can break data extraction","Sandboxed execution prevents access to local filesystem and system commands; limited to in-memory operations","Code generation quality depends on LLM capability; generated code may have bugs or inefficiencies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.25,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=cognosys","compare_url":"https://unfragile.ai/compare?artifact=cognosys"}},"signature":"TPDoZPsSD2UKXLBHSuqJ8s1dH5EKivCUf5K5ZZoRgAFqPJHJnMEosz2TWK7TJNVVehYJo0leNIfAdubzfIOQDA==","signedAt":"2026-06-20T19:04:21.966Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cognosys","artifact":"https://unfragile.ai/cognosys","verify":"https://unfragile.ai/api/v1/verify?slug=cognosys","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"}}