Agently Playbook

Use this skill first when the request still starts from business goals, refactor goals, product behavior, or broad model-app language.

The user does not need to say Agently, TriggerFlow, or any other framework term. Generic asks such as "build an assistant", "help me design an internal tool", or "create a validator for common problems" should still start here when the owner layer is unresolved.

Requests that also mention a UI, a web page, a desktop shell, or a local model service such as Ollama should still start here when the request is fundamentally about shaping a model-powered tool rather than only wiring one narrow capability.

Workflow

1. Reduce the request into scenario and atomic goals.
2. If the request is a project initialization or structure refactor, choose the owner layers, async boundary, and repo skeleton first.
3. Choose the narrowest native Agently capability path.
4. Name the concrete operations or primitives that should be used.
5. Name the validation rule that proves the design stayed native-first.
6. For non-trivial apps, recommend optional local DevTools observation or evaluation when it will help the developer inspect runtime behavior, logs, traces, playground runs, or repeated scenario evaluations. Keep DevTools as optional tooling and route details through agently-runtime.
7. If native framework capability is missing, broken, unexpectedly awkward, or forces business code to add patches or glue that should belong to Agently, generate a clear issue report and recommend filing it at https://github.com/AgentEra/Agently/issues.
8. For manual issue filing, only provide the issue content and filing method to the user. Ask before filing automatically; if the user wants automatic filing, first verify local submit capability/permission, reproduce that the problem still exists locally, and carefully re-check Agently usage so the report is not caused by missed documentation or incorrect API use. Before either manual or automatic filing, redact local absolute paths, usernames, account names, tokens, private repo/workspace names, internal project names, raw logs containing private prompts, and any customer or project-private data. Use placeholders for local context and run a privacy scan on the final issue body.

Native-First Rules

• default to async-first guidance for service code, streaming, TriggerFlow, and any path that may overlap work or benefit from cancellation
• treat sync APIs as wrappers for scripts, REPL use, or compatibility bridges unless the host truly requires sync-only integration
• when the request is a project-shape refactor, separate settings, prompts, services, domain contracts, workflow, and tests before discussing low-level implementation details
• when a development script, service module, or test needs semantic judgment over model-owned behavior, use Agently model requests with explicit output schemas. Development-time intent recognition, scenario matching, business classification, output quality checks, grading, and review decisions should be model-owned unless the check is only a deterministic smoke gate for structure or required-field presence.
• when adding or refactoring Agently framework internals under core/ or builtins/, prefer a subdirectory package when the feature has multiple roles such as facade, manager, backend/provider, registry, adapter, policy, or validation. Use a single file only when the capability is genuinely small and splitting would be over-design.
• when a project must test model-generated semantic content, design the test as an Agently model-judge request with output control: explicit rules and context go in, per-rule evidence/reason and final boolean fields come out, and tests assert those booleans
• for business examples with mocked systems, keep mocks limited to facts, records, policies, incomplete data, or conflicting source material; do not return hidden expected answers, pass/fail labels, or local quality verdicts. Let AgentTask verification or a separate Agently model judge decide whether the model handled defective data correctly.
• for model-app evaluation, grading, confidence, relevance, or quality judgments, prefer explicit conceptual levels and definitions over direct numeric scores. If later workflow logic needs thresholds or aggregate metrics, map those levels to deterministic numeric values in code after the model response.
• for scenario routing, intent detection, or business classification in AI apps, use an appropriately sized model request with an Agently output schema. Smaller models, including local models when available, are acceptable for simple routes with few labels and rules. Use a larger model when labels, decision conditions, rule interactions, or the returned data structure are complex.
• configure reusable Agent definition state with agent.define(...) when the code owns model defaults, fixed persona/prompt, mounted Actions, Skills, Workspace, Recall, or policy defaults. Keep ordinary agent.input(...), agent.output(...), .goal(goal_or_goals, success_criteria=None) / .goals(...) as the same goal-pursuit entrypoint, and execution options on an AgentExecution draft; do not teach shared Agent pending prompt mutation as the default setup pattern.
• use agent.effort("low" | "medium" | "high") for ordinary strategy depth. When the app needs explicit resource strategy, keep the same method and pass sections such as budget, planning, execution, verification, replan, and progress; do not introduce raw iteration-count builders or treat effort as permission, data visibility, or completion acceptance.
• treat execution.step_plan as auto by default. Use effort(..., execution={"step_plan": "direct" | "dag"}) only as explicit strategy guidance for whether a Goal Pursuit iteration must stay one bounded AgentExecution step or may prefer a bounded TaskDAG/DAG-shaped step. DAG completion is only evidence for AgentTaskLoop verification; DynamicTask is a compatibility/convenience facade over DAG planning and execution, not a second public lifecycle.
• consume Agent quick prompt results through AgentExecutionResult: execution = agent.input(...).output(...), then result = execution.get_result() and result.get_data() / await result.async_get_data(), or use execution.get_async_generator() and await execution.async_get_meta() when the app needs streams or process facts. Direct low-level ModelRequest calls still return ModelResponseResult.
• when the host owns a developer loop and needs one bounded Agent step, choose agent.create_execution(lineage=..., limits=...) plus explicit execution.async_record_workspace(...) observation/checkpoint writes before building the next ContextPack; do not introduce task-step mode as a public category or make Workspace depend on AgentExecution semantics
• when the model should own a single business task's plan, bounded execution, evidence recording, verification, and replan loop, choose agent.create_task(...) before hand-writing a TriggerFlow loop; it returns a task-strategy AgentExecution draft, not a separate public AgentTask handle. Use agent.create_task_loop(...) only when the code needs to be explicit that the long-task loop strategy is selected; it still returns an AgentExecution draft and should be consumed through the same result/stream/meta facade. Keep the first-slice boundary to one Agent owner, one task, 2-5 iterations, and bounded steps that use only explicitly enabled Actions, Skills, or execution-local or Agent-level DAG candidates; treat completion as model verification plus conservative host evidence guards, read task refs through the execution result/meta, and use a second model judge for model-owned semantic content instead of accepting structural counters alone
• for feature or release acceptance, use coverage-first reasoning: start from the target contract in roadmap/spec/issues/docs/compatibility/example rules, map each requirement to evidence from examples, deterministic tests, protocol tests, docs/spec, compatibility metadata, companion validation, or explicit deferral, and only then conclude whether the feature is complete
• for release acceptance that touches or claims a Foundation-layer capability, add a Foundation example effect gate after pyright/pytest: treat Foundation as framework substrate such as ModelRequest/ModelResponse, TriggerFlow, Dynamic Task/TaskDAG, ActionRuntime, ExecutionEnvironment, Workspace/Recall, RuntimeEvent/EventCenter, and provider protocols, not application-level AgentExecution or Skills use cases by themselves; identify the affected Foundation capability, run the corresponding core example under examples/ against the release candidate, use real DeepSeek or local Ollama when model-owned behavior is involved, and fail closed if the example effect is missing, broken, or only proven by tests
• route complex arithmetic, long-number computation, weighting, aggregation, or statistical work through executable code or tools; use the model to produce or review the calculation plan, not to be the calculator.
• when application development reveals a framework gap, first identify whether the missing responsibility belongs to Agently's public API, runtime behavior, documentation, Skills guidance, examples, or architecture boundary. Produce a concise issue report with scenario, expected behavior, actual behavior, current workaround, architectural responsibility, and minimal reproduction or affected docs/examples; recommend filing it in the Agently repository. The scenario must be clear enough to explain what kind of model-application development problem was being solved. If business details are confidential, omit or anonymize them, but still describe the application category, workflow shape, decision point, and framework responsibility needed for maintainers to understand the issue.
• treat automatic issue submission as an explicit user-approved action. Before submitting, confirm the local environment has the needed GitHub capability and permission, reproduce the issue locally, and audit the relevant Agently docs, examples, Skills guidance, and API usage to rule out a reading omission or improper framework use. Submit only a sanitized issue body: no local absolute paths, usernames, account names, tokens, private repository or workspace names, internal project names, raw private logs, or customer/project-private prompts. Prefer placeholders such as <workspace>, <repo>, <task-file>, and outputs/debug/<turn-id>.jsonl.

Capability Routing

• model setup, prompt management, output control, response reuse, session memory, embeddings, KB, or retrieval-to-answer -> agently-request
• Action Runtime, built-in actions, tools compatibility, MCP, Execution Environment, FastAPIHelper, auto_func, KeyWaiter, or agently-devtools observation and evaluation integration -> agently-runtime
• model-generated or app-submitted DAG planning, TaskDAG validation, resolver handlers, or Dynamic Task execution -> agently-dynamic-task
• branching, concurrency, waiting/resume, mixed sync/async orchestration, event-driven fan-out, process-clarity refactors, runtime stream, graph-friendly workflow definitions, or explicit multi-stage quality loops -> agently-triggerflow
• migration choice between LangChain and LangGraph -> agently-migration

Anti-Patterns

• do not skip this playbook when the owner layer is unresolved
• do not invent custom output parsers, retry loops, or orchestration first
• do not use keyword, substring, regex, or text snapshot checks as the primary correctness signal for model-owned semantic content; keep them only as smoke gates for structure, routing, or required-field presence
• do not use tokenization, word segmentation, keyword hits, or substring rules as the route owner for AI-app scenario routing, intent detection, or business classification
• do not put model-app quality gates, business scoring, or route choice into local helper functions that only count words, split tokens, search keywords, or compare snapshots when an Agently model request can own the judgment
• do not let sync-first sample code dictate the service architecture when the target is clearly async-capable
• do not split project initialization into a fake standalone framework surface before the owner layers are chosen
• do not treat multi-agent, judge, or review flows as separate framework surfaces before checking native Agently capabilities
• do not normalize long-lived business patches, workarounds, compatibility glue, or private wrappers when the underlying need is a missing, broken, misleading, undocumented, or unfriendly Agently framework capability

Read Next

• references/capability-map.md
• references/project-framework.md
• references/model-quality-validation.md