# Agenci i orkiestracja

EN: **Agents and orchestration** · id `agentsnode`

Path: [AI Atlas](https://aiatlasgraph.com/llms.txt) › [Aplikacje LLM i agenci](https://aiatlasgraph.com/t/applications.md) › Agenci i orkiestracja

## Description

- PL: Pętle działania, delegowanie i sterowanie wykonaniem.
- EN: Action loops, delegation and control over execution.

## In one sentence

- PL: To programy, które wykonują zadania etapami, oraz sposoby koordynowania ich pracy.
- EN: These programs take steps toward a goal, with controls that organise their work.

## Problem it solves

- PL: Jedna odpowiedź nie wystarcza, gdy trzeba sprawdzić informacje, podjąć decyzje i wykonać kilka zależnych od siebie czynności.
- EN: A single answer cannot finish a task that requires checking information, making decisions and carrying out several connected actions.

## What it does

- PL: Po każdym kroku sprawdzają wynik i decydują, czy kontynuować, użyć narzędzia, czy przekazać pracę dalej.
- EN: After each step, they inspect the result and decide whether to continue, use a tool or hand work to another assistant.

## Everyday example

- PL: Przygotowując wyjazd, asystent porównuje połączenia, sprawdza noclegi i przedstawia plan do twojej oceny.
- EN: An assistant planning your trip compares travel options, checks accommodation and brings you a plan to review.

## This concerns you if you…

- PL: zlecasz asystentowi zadania wymagające kilku kroków
- PL: chcesz wiedzieć, kto koordynuje pracę kilku asystentów
- EN: give an assistant tasks that need several steps
- EN: want to know how several assistants coordinate their work

## Keywords

ReAct · agent harness · agent loop · handoffs · plan-and-execute · task delegation · multi-agent systems · supervisor pattern · stopping criteria · token budgets

## How it works

- PL: Agent wybiera działanie na podstawie celu, bieżącego stanu i wyników poprzednich kroków. Środowisko wykonawcze uruchamia narzędzia, dopisuje obserwacje i steruje delegowaniem, budżetem oraz warunkiem zakończenia pętli.
- EN: An agent selects an action from its goal, current state and previous results. The runtime executes tools, records observations and controls delegation, budget and the condition for ending the loop.

## Limits

- PL: Błędy mogą kumulować się między krokami. Swobodne planowanie utrudnia przewidywanie kosztu i odtwarzanie przebiegu; potrzebne są ślady wykonania, limity działań i testy całych zadań.
- EN: Errors can accumulate across steps. Open-ended planning makes cost prediction and replay harder; execution traces, action limits and evaluations of complete tasks are needed.

## Key terms

- PL: **obserwacja**: Wynik działania, który wraca do kontekstu agenta.
- PL: **harness**: Środowisko sterujące wykonaniem, narzędziami i ograniczeniami agenta.
- PL: **warunek zakończenia**: Sprawdzalna reguła określająca, kiedy agent przestaje działać.
- EN: **observation**: An action result returned to the agent's context.
- EN: **harness**: The runtime controlling an agent's execution, tools and constraints.
- EN: **stopping condition**: A checkable rule determining when an agent stops acting.

## Know first

- [Narzędzia i integracje (Tool use and integration)](https://aiatlasgraph.com/t/tools.md)
- [Workflow i udział człowieka (Workflows and human-in-the-loop)](https://aiatlasgraph.com/t/workflows.md)

## Examples

- PL: Harness · hand-offs · subagenci
- EN: Harness · hand-offs · subagents

## Subtopics (16 direct, 35 in total)

- [Agenci przy komputerze (Computer and browser use)](https://aiatlasgraph.com/t/computeruse.md): 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)](https://aiatlasgraph.com/t/sacua.md): 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)](https://aiatlasgraph.com/t/softwaregym.md): Pipelines that automate synthesis, resetting and verification of executable training environments for computer-use agents.
- [Agenci w tle (Ambient and scheduled agents)](https://aiatlasgraph.com/t/ambient.md): Agents that wake up on their own: on a schedule or when something changes.
- [Autor i recenzent (Maker–checker review)](https://aiatlasgraph.com/t/makerchecker.md): 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)](https://aiatlasgraph.com/t/dllmdeepresearch.md): 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)](https://aiatlasgraph.com/t/dfast.md): 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)](https://aiatlasgraph.com/t/hybridagents.md): An agent dynamically switches between latent-space reasoning and text for tool calls and answers.
- [Orkiestracja subagentów (Subagent orchestration)](https://aiatlasgraph.com/t/subagents.md): Deciding when to launch subagents and how to hand work to them.
  - [Rynkowa samoorkiestracja rojów agentów (Market-based post-training for decentralized agent swarms)](https://aiatlasgraph.com/t/mpt.md): 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))](https://aiatlasgraph.com/t/mass.md): 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)](https://aiatlasgraph.com/t/agima.md): 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)](https://aiatlasgraph.com/t/a2a.md): An agreed way for agents from different systems to talk.
- [Role i tożsamość agentów (Agent roles and identity)](https://aiatlasgraph.com/t/agentroles.md): A named agent with a scope and standing instructions instead of an anonymous session.
- [Routing między modelami (Model and harness routing)](https://aiatlasgraph.com/t/routing.md): 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)](https://aiatlasgraph.com/t/treeflex.md): 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)](https://aiatlasgraph.com/t/selfdirection.md): 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)](https://aiatlasgraph.com/t/agentskills.md): A catalog of folders with metadata, instructions and resources, loaded in layers according to the task match.
  - [Format pliku umiejętności (Skill file format)](https://aiatlasgraph.com/t/skillformat.md): 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)](https://aiatlasgraph.com/t/skillscripts.md): Executable code bundled with a skill and run as a tool instead of being generated into context.
  - [Stopniowe wczytywanie umiejętności (Progressive disclosure of skills)](https://aiatlasgraph.com/t/skilldisclosure.md): Three-level skill loading: metadata at startup, body after selection, resources on demand.
- [Uprząż agenta (Agent harness)](https://aiatlasgraph.com/t/agentharness.md): 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)](https://aiatlasgraph.com/t/agenttimebudget.md): Harness and learning mechanisms that help an agent adhere to a wall-clock time budget.
  - [Kompozycyjne przeszukiwanie uprzęży (Compositional harness search)](https://aiatlasgraph.com/t/harnesscomposition.md): Modular harness with pools of implementations; a contextual bandit selects combinations for evaluation.
  - [Mapa dokumentów dla agenta (Repository map for agents)](https://aiatlasgraph.com/t/harnessdocs.md): 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)](https://aiatlasgraph.com/t/harnessinvariants.md): 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)](https://aiatlasgraph.com/t/sessionhandoff.md): Passing state between context windows through a log, a feature list and change history, not through compaction alone.
  - [Uczenie uprzęży agentów (Harness learning)](https://aiatlasgraph.com/t/harnesslearning.md): Meta-learning over harness code: a proposer generates edits, reinforcement learning on execution outcomes.
    - [Adaptacja uprzęży podczas RL (Harness adaptation during agentic reinforcement learning)](https://aiatlasgraph.com/t/harnessadaptrl.md): RL in which the policy and harness adapt together based on training trajectories.
    - [Koewolucja modelu i uprzęży agenta (Weight-harness co-evolution)](https://aiatlasgraph.com/t/weightharnesscoevo.md): A single policy acts as proposer and reflector; harness edit logs train its weights.
    - [Rekurencyjna uprżąż z rewizją wsteczną (Recurrent harness with backward revision)](https://aiatlasgraph.com/t/reharness.md): 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)](https://aiatlasgraph.com/t/harnessscaling.md): Scaling agentic RL by training on diverse harness configurations instead of one fixed one.
  - [Zgodność intencji z wykonaniem narzędzia (Tool-call intent–execution correspondence)](https://aiatlasgraph.com/t/intentexecution.md): 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)](https://aiatlasgraph.com/t/blackboard.md): 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)](https://aiatlasgraph.com/t/stoploss.md): External small monitor model trained on completed trajectories and verifier outcomes that predicts failure and aborts the run.

## Related topics

Untyped, symmetric "see also" links.

- [Agenci badań prawnych (Agentic legal research)](https://aiatlasgraph.com/t/legalresearchagents.md)
- [Agenci głosowi (Real-time voice agents)](https://aiatlasgraph.com/t/voiceagents.md)
- [Agenci kodujący (AI-assisted coding)](https://aiatlasgraph.com/t/aicoding.md)
- [Bezpieczeństwo agentów: uprawnienia i izolacja (AI agent security)](https://aiatlasgraph.com/t/agentsecurity.md)
- [Ewaluacja agentów zgodna z przepływem zadań (Task-flow aligned agent evaluation)](https://aiatlasgraph.com/t/taskfloweval.md)
- [Ewaluacja RAG i agentów (RAG and agent evaluation)](https://aiatlasgraph.com/t/agenteval.md)
- [Planowanie automatyczne (PDDL) (Automated planning (PDDL))](https://aiatlasgraph.com/t/classicalplanning.md)
- [Planowanie zadań robota z modelem językowym (Language-model task planning for robots)](https://aiatlasgraph.com/t/llmtaskplanning.md)

## Courses

- [Agentic AI](<https://www.deeplearning.ai/courses/agentic-ai>)

## Sources

- [Hugging Face · Agents Course](<https://huggingface.co/learn/agents-course/en/unit0/introduction>) `agents` · reference: Agenci, narzędzia, frameworki, agentic RAG i ewaluacja.
- [FSDL · LLM Bootcamp (2023, archiwum)](<https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/>) `bootcamp` · reference: Aplikacje LLM, prompting, retrieval, UX i LLMOps.
- [DeepLearning.AI — course catalogue](<https://www.deeplearning.ai/courses>) `dlai` · course catalogue: 42 topic labels with counts, about 130 courses.
- How to build your first team of agents `machina-x-agents` · X article · sponsored (Raft) · by Machina (X): Original link not found; file saved by the user. Structure only — no statistics, no product claims.

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AI Atlas revision 122 (2026-10-30) · HTML: https://aiatlasgraph.com/t/agentsnode · index: https://aiatlasgraph.com/llms.txt · everything: https://aiatlasgraph.com/llms-full.txt · updated 2026-10-30