agently-playbook

name

agently-playbook

description

Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow from a business scenario or common problem statement, including project-structure refactors or starter skeletons that may separate model setup, prompt config, and orchestration, even if the request also mentions a UI, app shell, or local model service such as Ollama, and it is still unclear whether the solution should stay a single request, add supporting capabilities, or become orchestration. The user does not need to mention Agently explicitly.

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. 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.
7. 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 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 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.
• 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 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