AgentEra

Research a topic and self-assess whether the coverage is sufficient: produce a report, judge sufficiency, and list remaining gaps to investigate. Use for adaptive/agentic research and iterative investigation.

Produce a deep, structured research report on a topic: decompose into key dimensions, analyze each with evidence and reasoning, synthesize cross-cutting insights, and surface open questions. Use for deep research, analysis, and literature/landscape reviews.

Audit a vendor agreement or contract: extract key clauses, check that required clause categories are present, flag compliance gaps and risks, and emit a structured audit summary. Use for compliance, contract review, audit, and vendor agreement requests.

Analyze an infrastructure incident alert and produce a structured response plan plus an executable on-call runbook. Use for incident, alert, on-call, and runbook requests.

Draft launch communications: an announcement / press release, an internal support FAQ, and a customer-facing feature summary. Use for launch comms, press release, announcement, and messaging.

Identify, categorize, and prioritize product launch risks, producing a risk matrix (probability × impact) with mitigation strategies. Use for launch risk, risk register, and go-to-market risk review.

Analyze product market positioning, competitive landscape, and unique value proposition. Use for positioning, market analysis, and differentiation.

Generate a structured market research brief from a feature idea and supplied business context: market landscape, competitor intelligence, opportunity assessment, and a go-to-market recommendation. Use for market, research, competitor, and product launch requests.

Use when the user wants Agently runtime extension capabilities: Action Runtime, built-in action packages, legacy tool compatibility, MCP access, Execution Environment lifecycle, FastAPIHelper or streaming API exposure, auto-function helpers, KeyWaiter, or optional agently-devtools observation, evaluation, and playground integration.

Use when the user is shaping Agently request-side behavior: model setup, settings files, prompt management, structured output, response reuse, streaming consumption, session memory, embeddings, knowledge-base indexing, retrieval, or retrieval-backed answers within one request family.

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.

Use when the user needs Agently Dynamic Task, model-generated or app-submitted DAG planning, TaskDAG validation, DynamicTaskResolver handlers, or TaskDAGExecutor execution through Agently.create_dynamic_task. Dynamic Task is a first-class Agently API that uses TriggerFlow as an execution substrate.

Use when the user needs workflow orchestration such as branching, concurrency, approvals, waiting and resume, runtime stream, restart-safe execution, mixed sync/async function or module orchestration, event-driven fan-out, process-clarity refactors that make stages explicit, performance-oriented refactors that collapse split requests, or workflow definitions and chunk-level runtime metadata that must stay visible for debugging and visualization. The user does not need to say TriggerFlow explicitly.

Use when the user wants Action Runtime or tool use, MCP access, HTTP or streaming API exposure, auto-function helpers, wait-for-key behavior, or optional `agently-devtools` observation, evaluation, and playground integration through Agently-native extension surfaces rather than custom wrappers first.

Use when the user wants embeddings, vector indexing, retrieval, or retrieval-backed answers, including embedding-agent setup, Chroma-backed collections, collection add/query, and KB-to-answer flows.

Use when a migration is already known to stay on the LangChain agent side, including agent setup, tools, structured output, retrieval, and short-term memory.

Build complex agent and intelligent system services with Agently (Model control + TriggerFlow).

Agent setup, prompt layering, routing, and info injection patterns.

FastAPI service wrappers for Agently Agent + TriggerFlow (POST, SSE, WebSocket).

Translate LangChain/LangGraph patterns into Agently code (model control + TriggerFlow).

Structured output patterns with ensure_keys, ordering, and instant streaming.

Streaming outputs, runtime streams, and ReAct-style tool loops.

Event-driven TriggerFlow orchestration patterns and branching.