Aplikacje LLM i agenci

LLM applications and agents · id applications

Budowanie systemów korzystających z modeli i narzędzi.

Building systems that use models and tools.

In one sentence

To dziedzina budowania programów, w których sztuczna inteligencja czyta, rozmawia i korzysta z narzędzi.

This field builds programs that use artificial intelligence to read information, hold conversations and use tools.

Problem it solves

Musisz ręcznie szukać informacji i przenosić je między programami, żeby załatwić nawet prostą sprawę.

Even a simple task can require you to find information and move it between several programs by hand.

What it does

Łączy model językowy z dokumentami i narzędziami oraz ustala zasady, które kierują jego pracą.

It connects a language model to documents and tools, with rules that control which information and actions it can use.

Everyday example

Prosisz firmowego asystenta o znalezienie warunków zwrotu i przygotowanie odpowiedzi dla klienta.

You ask a workplace assistant to find the returns policy and draft a reply to a customer.

This concerns you if you…

• chcesz załatwiać sprawy przez rozmowę z asystentem
• przenosisz informacje między dokumentami a firmowymi programami
• want to get things done by talking to an assistant
• move information between documents and workplace software

Subtopics

• Agenci i orkiestracja (Agents and orchestration): Action loops, delegation and control over execution.
  • Agenci przy komputerze (Computer and browser use): An agent clicking in a browser and on a desktop.
    • Granica zaufania SA-CUA dla agentów komputerowych (SA-CUA trust boundary for computer-use agents): A dual-model architecture that masks the screen by default, reveals trusted regions using accessibility-tree rules, and separately endorses values from untrusted content.
    • Syntetyczne środowiska komputerowe do treningu (Synthetic computer-use environments for agent training): Pipelines that automate synthesis, resetting and verification of executable training environments for computer-use agents.
  • Agenci w tle (Ambient and scheduled agents): Agents that wake up on their own: on a schedule or when something changes.
  • Autor i recenzent (Maker–checker review): No agent grades its own work; a second one assumes the output is broken and looks for where.
  • Dyfuzyjne modele językowe w agentach badań głębokich (Diffusion language models in deep research agents): Deep-research systems in which a diffusion language model powers model-bound stages such as planning, decomposition and report synthesis.
    • Wczesna ekstrakcja podzapytań ze stanów odszumiania (Early subquery extraction from diffusion denoising states): A decomposition mode that reads intermediate denoising states of a diffusion model and extracts usable subqueries before generation finishes.
  • Hybrydowe agenty rozumujące (Hybrid reasoning agents): An agent dynamically switches between latent-space reasoning and text for tool calls and answers.
  • Orkiestracja subagentów (Subagent orchestration): Deciding when to launch subagents and how to hand work to them.
    • Rynkowa samoorkiestracja rojów agentów (Market-based post-training for decentralized agent swarms): A post-training framework in which decentralized agent swarms jointly learn action policies and self-orchestration through bids.
    • Samonadzór wieloagentowy (MASS) (Multi-agent self-supervision (MASS)): A recursive self-improvement method in which one model alternates between improving multi-agent workflows and fine-tuning its own weights on selected trajectories.
  • Planowanie przez wyobrażone przyszłe stany (Planning with imagined future observations): A technique combining policy and world model that generates visual consequences of hypothetical actions and selects a plan by comparing future states.
  • Protokoły agent–agent (Agent-to-agent protocols): An agreed way for agents from different systems to talk.
  • Role i tożsamość agentów (Agent roles and identity): A named agent with a scope and standing instructions instead of an anonymous session.
  • Routing między modelami (Model and harness routing): Matching the model and runtime to the kind of task and its cost.
  • Samodzielna nawigacja drzewa stanów agenta (Agent self-navigation in checkpointed search trees): A technique in which an agent saves intermediate states as a tree and chooses which node to resume, separating tree-level reflection from tool execution.
  • Trwałe naprawy agentów przez adaptery lekcji (Persistent agent repair with lesson adapters): A mechanism in which an agent, after failure, distils a lesson from its trajectory into a lightweight LoRA adapter while keeping base weights frozen.
  • Umiejętności agentów (Agent skills): A catalog of folders with metadata, instructions and resources, loaded in layers according to the task match.
    • Format pliku umiejętności (Skill file format): A folder with an instruction file whose header has at least a name and description, matching the folder name.
    • Skrypty w paczce umiejętności (Bundled skill scripts): Executable code bundled with a skill and run as a tool instead of being generated into context.
    • Stopniowe wczytywanie umiejętności (Progressive disclosure of skills): Three-level skill loading: metadata at startup, body after selection, resources on demand.
  • Uprząż agenta (Agent harness): The loop, tools, context and constraints that turn a model call into an agent that carries out work.
    • Budżety czasu dla agentów (Time-budgeted agent control): Harness and learning mechanisms that help an agent adhere to a wall-clock time budget.
    • Kompozycyjne przeszukiwanie uprzęży (Compositional harness search): Modular harness with pools of implementations; a contextual bandit selects combinations for evaluation.
    • Mapa dokumentów dla agenta (Repository map for agents): The agent's entry point is a short map to versioned documents, not a full manual in one file.
    • Mechaniczne reguły środowiska agenta (Mechanical invariants for agent environments): Architectural and style invariants enforced by tests and linters, with a repair hint in the error.
    • Przekazanie pracy między sesjami (Cross-session handoff for agents): Passing state between context windows through a log, a feature list and change history, not through compaction alone.
    • Uczenie uprzęży agentów (Harness learning): Meta-learning over harness code: a proposer generates edits, reinforcement learning on execution outcomes.
      • Adaptacja uprzęży podczas RL (Harness adaptation during agentic reinforcement learning): RL in which the policy and harness adapt together based on training trajectories.
      • Koewolucja modelu i uprzęży agenta (Weight-harness co-evolution): A single policy acts as proposer and reflector; harness edit logs train its weights.
      • Rekurencyjna uprżąż z rewizją wsteczną (Recurrent harness with backward revision): A harness that adapts textual state by reading and writing reflections, then revising them from the outcomes of their downstream use.
      • Skalowanie uprzęży w agenticznym RL (Harness-diversity scaling for agentic reinforcement learning): Scaling agentic RL by training on diverse harness configurations instead of one fixed one.
    • Zgodność intencji z wykonaniem narzędzia (Tool-call intent–execution correspondence): The property that the action actually executed by a tool matches the action denoted by the call under the tool contract.
  • Wspólna przestrzeń agentów (Shared agent workspace): A place where agents see each other’s work. Classic name: blackboard architecture.
  • Wyuczone wczesne zatrzymywanie nieudanych uruchomień agentów (Learned early stopping of failing agent runs): External small monitor model trained on completed trajectories and verifier outcomes that predicts failure and aborts the run.
• Agenci kodujący (AI-assisted coding): Writing, reviewing and maintaining code with agents.
  • Kernele pisane przez LLM (LLM-generated kernels): Models and agents that write and speed up GPU code themselves — with RL and evolutionary search.
  • Migracja i modernizacja kodu z AI (AI-assisted code migration and modernisation): Models and agents rewrite large amounts of existing code: library upgrades, interface changes, translation between languages.
  • Podpowiadanie kodu w edytorze (Inline code completion (fill-in-the-middle)): A model writes the next lines or blocks of code at the cursor; systems trained with fill-in-the-middle can use code both before and after the gap.
  • Programowanie od specyfikacji (Spec-driven development with coding agents): A structured specification is created first, often together with a technical plan and task list; these artifacts guide the agent and may remain a reference point for later changes.
  • Przegląd kodu przez AI (AI code review): A model analyses a code change (pull request) and leaves comments on bugs, risks and style before human review.
  • Sterowanie przed pierwszą edycją kodu (Pre-edit planning intervention for coding agents): A single planning intervention before the first source edit in coding agents.
  • Vibe coding: tworzenie bez przeglądania kodu (Vibe coding (creating software without reviewing generated code)): Iterative software creation through prompts and behavioral testing without reading or understanding the generated code; unlike responsible AI-assisted programming, the generated changes are not reviewed.
• Asystenci i chatboty (Chatbots and assistants): Conversation as the way a model is delivered.
• Frameworki agentowe (Agent frameworks): Ready-made skeletons for building agents and LLM applications.
• Generatywne UI (Generative UI): Interface built by the model as the conversation goes.
• Narzędzia i integracje (Tool use and integration): Connecting a model to functions and external systems.
  • MCP (Model Context Protocol): A protocol for exposing tools and context to model-based applications.
  • Structured outputs i walidacja (Structured outputs and validation): Forcing the shape of an answer and checking it before use.
• Pamięć i stan (Memory and state): Keeping information between steps and conversations.
  • Agentyczna pamięć z dynamicznymi powiązaniami (Agentic memory with dynamic linking): Technique for agentic memory organisation through dynamic note creation and linking inspired by the Zettelkasten method.
  • Hierarchiczne zarządzanie kontekstem wirtualnym (Virtual context management with hierarchical memory): Technique for managing virtual context via a memory hierarchy and agent interrupts, inspired by operating systems.
  • Nietrwałe wskazówki w pamięci agentów (Anti-Persistence for agent memory): A memory design that accumulates trajectories and outcomes, synthesizes temporary guidance on demand, and discards it after execution.
  • Pamięć proceduralna (Procedural memory): Saved recipes for solved problems, ready for reuse.
  • Pętla samodoskonalenia (Reflection and feedback loops): Reasons for rejections are written back into instructions and memory, so the same mistake does not survive twice.
• Prompty i kontekst (Prompting and context engineering): Choosing the instructions and information available at call time.
  • Budowa polecenia: instrukcje, rola i znaczniki (Prompt structure: instructions, roles and delimiters): Laying out a prompt with clear instructions, purpose, a role in the system prompt, data sections separated by delimiters and an output format description.
  • Programowa optymalizacja promptów (Programmatic prompt optimisation): Automatically tuning prompts, and sometimes weights, against an evaluation score.
  • Przykłady w poleceniu (few-shot) (Few-shot prompting and in-context learning): A few input–output pairs placed in the prompt show the model the task and answer format without changing its weights.
  • Zarządzanie oknem kontekstu (Context window management: placement, compaction and context rot): Deciding which tokens enter a limited context and in what order: placement, trimming, summarisation and external notes.
• Przetwarzanie dokumentów (Document AI): Pulling content and structure out of documents.
  • Analiza układu i parsowanie dokumentów (Document layout analysis and parsing): Detecting page blocks (headings, paragraphs, tables, figures, footnotes), determining reading order and exporting structured text.
  • Wyciąganie pól z formularzy i faktur (Key information extraction from forms and receipts): Mapping document fragments to named fields (e.g. tax ID, gross amount, date) using text, its position and the page's appearance.
  • Wyciąganie tabel z dokumentów (Table extraction and structure recognition): Detecting a table on a page, reconstructing its rows, columns, headers and merged cells, and saving it in a machine-readable format.
• Workflow i udział człowieka (Workflows and human-in-the-loop): Combining predictable stages with model and human decisions.
  • Architektura zdarzeniowa (Event-driven AI): Pipelines triggered by events rather than by a click.
  • Łańcuchy promptów (Prompt chaining): A complex task is split into a fixed sequence of simpler model calls in which one step's output is the next step's input.
• Wyszukiwanie i RAG (Retrieval-augmented generation): Fetching knowledge and using it while answering.
  • Bazy wektorowe (Vector databases): Storing and searching by vectors.
  • Dzielenie dokumentów (Document chunking): Cutting documents into indexable pieces, preferably by structure and with metadata.
  • Hierarchiczna nawigacja dokumentów modelami decyzyjnymi (Hierarchical document navigation with decision models): Navigation through a corpus hierarchy in which a decision model evaluates branches jointly at each level without autoregressive text generation.
  • Przekształcanie zapytań (Query transformation): Rewriting the user's question before it reaches the search.
  • Reranking i fuzja wyników (Reranking and result fusion): Re-ordering retrieved chunks before they reach the model, for relevance and diversity.
  • Wyszukiwanie hybrydowe (Hybrid retrieval): Combining vector similarity with keyword matching, sometimes several vectors per document.
  • Zapytania do danych strukturalnych (Query construction for structured data): Translating a question into a database query language when the answer lives in tables or a graph.

Sources

• Hugging Face · Agents Course · reference: Agenci, narzędzia, frameworki, agentic RAG i ewaluacja.
• FSDL · LLM Bootcamp (2023, archiwum) · reference: Aplikacje LLM, prompting, retrieval, UX i LLMOps.
• DeepLearning.AI — course catalogue · course catalogue: 42 topic labels with counts, about 130 courses.