Use when the user wants Agently runtime extension capabilities: Action Runtime, built-in action packages, legacy tool compatibility, MCP access, ExecutionResource lifecycle, FastAPIHelper or streaming API exposure, auto-function helpers, KeyWaiter, or optional agently-devtools observation, evaluation, and playground integration.
Instalação
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Use when the user wants Agently runtime extension capabilities: Action Runtime, built-in action packages, legacy tool compatibility, MCP access, ExecutionResource lifecycle, FastAPIHelper or streaming API exposure, auto-function helpers, KeyWaiter, or optional agently-devtools observation, evaluation, and playground integration.
Agently Runtime
Use this skill when the app needs model-callable capabilities, managed execution
dependencies, service exposure, or development/runtime tooling around an
existing request or workflow design.
If the owner layer is still undecided, start with agently. If the
problem is multi-step orchestration, use agently-triggerflow first and return
here for Actions, ExecutionResource, service, or DevTools details.
observation, evaluation, playground, local logs, or agently-devtools -> references/devtools.md
Native-First Rules
prefer @agent.action_func and agent.use_actions(...); tool_func, use_tool, use_tools, and agently.builtins.tools are compatibility surfaces
use built-in web packages through from agently.builtins.actions import Search, Browse and mount with agent.use_actions(Search(...)) / agent.use_actions(Browse(...))
do not invent enable_search(...); Search/Browse proxy, timeout, backend,
and transient retry configuration belongs to the package/executor, not
ExecutionResource. Registered Browse actions fail closed with structured
diagnostics when all backends fail; direct Browse.browse(...) remains a
text-returning compatibility helper. Browse defaults to Jina Reader ->
Playwright -> BS4 -> restricted curl, where curl is an internal normalized-URL
fallback and not model-visible shell execution. Jina Reader is an external
URL-to-Markdown first pass and Browse automatically tries the official
alternate endpoint https://r.jinaai.cn/ when the primary Reader endpoint has
a transport or service failure; disable it with
Browse(enable_jina_reader=False, fallback_order=("playwright", "bs4", "curl"))
when that external service boundary is not acceptable. Browse should
try same-host protocol and canonical candidates before giving up, and remote
PDF/Office/image/download bytes should be materialized into the bound Workspace
with file refs rather than copied into the model hot path.
treat agent_task.heartbeat as observation only: AgentTask may emit it after
a quiet interval with no other stream items, but any normal progress,
snapshot, child-execution, delta, or phase event resets the quiet timer.
Heartbeats help consumers show liveness; they do not satisfy evidence,
hide stalls, or replace request/no-progress/task deadline timeouts.
default Agents and TriggerFlow executions expose lazy Foundation Workspace
bindings backed by the current session/script physical Workspace; Agent,
execution, and task records are logical partitions, while file actions use
lineage-scoped roots under
files/lineage/<root-kind>/<root-id>/.../<leaf-kind>/<leaf-id>/files; use
agent.use_workspace(...) or flow.create_execution(workspace=...) when
the app needs an explicit root, read-only mode, direct backend, or registered
provider, and keep in mind that ordinary model requests do not persist
automatically
local Workspace materialization writes AGENTLY_WORKSPACE.md at the physical
root and scoped files_root; external coding agents and file Actions should
treat that guide as the boundary note for editable files versus Workspace
internals, and should not expect the guide to be named README.md. Standard
editable file areas are downloads/ for materialized remote files,
artifacts/ for supporting generated evidence or non-primary deliverables,
and reports/ for user-facing readable deliverables. Use
workspace.file_area_path(...) instead of hand-building those roots when
application or framework code needs a contained path.
create application-owned shared Workspace instances with
Workspace(...) or Agently.create_workspace(...) and bind each participant
with agent.use_workspace(shared_workspace) or
flow.create_execution(workspace=shared_workspace) when Agents,
TriggerFlow executions, or service workers must share task information across
an explicitly selected domain; do not rely on separate explicit Workspaces to
communicate; use
flow.create_execution(workspace=False) only when an execution should have no
Workspace binding
move information between separate Workspaces in application or TriggerFlow
business logic by searching/reading the source Workspace, writing or
storing records in the destination Workspace, and linking refs as needed;
Workspace itself is not a cross-space messaging or replication protocol
for durable multi-turn task records, write through agent.workspace or
explicitly configure it with agent.use_workspace(...); for model-callable
local file actions, use agent.enable_workspace_file_actions(...), which
exposes the current Workspace file working tree and inherits
agent.workspace.files_root; for coding-agent style file work, use
agent.enable_coding_agent_actions(...) so reads, glob/grep search,
targeted edits, unified-diff patches, and full-file writes stay under
Workspace path, readback, and stale-guard semantics
Workspace file IO is handler-backed: workspace.read_file(...),
workspace.write_file(...), workspace.materialize_file(...), and
workspace.export_file(...) use registered WorkspaceFileIOHandler
implementations or Workspace-owned byte materialization. Workspace owns path
containment, file info, digests, refs, and diagnostics; handlers own format
parsing, optional dependencies, image preparation, and export rendering. Use
agent.enable_workspace_file_actions(write=True, export=True) only when the
model should receive an export_file Action.
use workspace.build_context(...) for Workspace-backed context building;
advanced model-assisted planners, vector retrieval, rerankers, and
compressors should plug into ContextPlanner, WorkspaceContextRetriever, or
ContextPackager instead of becoming WorkLoop or Action shortcuts
for explicit TriggerFlow loops, store structured observations and decisions
in Workspace, link decisions to evidence with workspace.link(...) or
workspace.link_evidence(...), keep large payloads behind
workspace.ref_envelope(...), recover state with
workspace.latest_checkpoint(...), bind durable execution ports by creating
the execution with flow.create_execution(workspace=workspace) or with
runtime_resources={"snapshot_store": workspace, "runtime_event_store": workspace},
and inspect backend wiring
with workspace.capabilities(); when restoring, read the snapshot state
through workspace.latest_checkpoint(...) / workspace.get_data(...) and
pass it back to TriggerFlow async_load(...) so pause/resume and DAG
join semantics stay owned by TriggerFlow; use
flow.declare_resource_requirement(..., resolver=..., provider_kind=..., config_ref=..., secret_ref=..., fail_policy=...) for importable resource
resolver descriptors when workers can reconstruct live clients from shared
host/plugin modules, relying on TriggerFlow load diagnostics for
missing resolver, unhealthy resource, policy-forbidden resource, fail-open,
and fail-closed cases; use pause_for(..., channel_id=..., provider_id=..., wait_mode=..., hot_wait_timeout=..., cold_persistence_policy=..., request_payload_schema=..., response_payload_schema=..., audit_metadata=...)
to persist ExternalWait provider/channel/schema/audit metadata, and put a
stable exchange_id in audit_metadata when Workspace RuntimeEvent records
should be queryable by exchange; when the host owns an approval router, queue,
or exchange transport, bind it with runtime resource key
execution_exchange_provider, and implement provider publish_request(...)
to return exchange_id, audit_metadata, or provider_metadata without
taking over TriggerFlow resume lifecycle; reusable host transports may also be
registered with agently.base.execution_exchange.register_provider(...), and
hosts should render normalized views from
project_pending_exchanges(execution) / project_execution_exchanges(execution)
instead of raw interrupts; connected ActionFlow/PolicyApproval endpoints may
resolve live exchanges with execution_exchange.async_respond(exchange_id, payload, actor=...), which still funnels through TriggerFlow
continue_with(...); use
execution.set_compaction_policy(...) for long-running
TriggerFlow executions that externalize large payloads behind Workspace or
provider artifact refs while keeping only compaction facts and retained
lineage anchors in the execution snapshot; use
workspace.put_snapshot(..., expected_state_version=...) for CAS guarded
snapshot writes, workspace.claim_lease(...) /
workspace.heartbeat_lease(...) / workspace.release_lease(...) for
provider-owned lease projection, and workspace.put_artifact_ref(...) for
large durable payload refs
for restart diagnostics in explicit workflows, persist compact RuntimeEvent
facts through a configured TriggerFlow runtime event store or explicit
workspace.append_runtime_event(...) calls, then query bounded ranges with
workspace.query_runtime_events(...); Workspace stores durable facts and refs,
while TriggerFlow still owns pause/resume, replay, DAG readiness, and
approval/exchange lifecycle semantics; durable RuntimeEvent records now carry
parent signal, aggregation scope, operator id, interrupt id, resume request
id, actor id, lease owner id, snapshot refs, and artifact refs
for ModelRequest observability, read payload.model_request_telemetry from
existing model.* RuntimeEvents when present; it is a compact diagnostic
payload for provider/model, attempt, run lineage, duration, raw usage,
normalized/estimated usage_summary, request URL, side-channel, and error
facts; terminal model.status may carry estimated input/output character
lengths without exposing raw request payload. It is not an input to route,
retry, verifier, quality, planner, or prompt logic
for webhook, approval, or external callback resume flows, pass a stable
resume_request_id and actor to execution.async_continue_with(...); the
TriggerFlow resume ledger records accepted, dispatched, and completed or
dispatch-failed phases; callbacks delivered to expired execution-local leases
fail fast before acceptance without writing resume ledger entries, while the interrupt carries an ExternalWait
request envelope for projection and restart diagnostics
AgentExecution-owned ActionFlow approval waits are projected as stream items
with path exchange.pending / exchange.resolved,
meta.stream_kind=="exchange", and value
{"action": "...", "exchanges": [ExecutionExchangeView, ...]}; frontend or
service consumers should treat these normalized views as the render contract
instead of inspecting policy-gate implementation details
for distributed TriggerFlow recovery, pass
require_distributed_provider=True when persisting snapshots; this must fail
closed unless the selected providers report CAS, lease, range-read,
retention, and RuntimeEvent sequence capabilities and expose matching
snapshot, lease, and artifact-ref methods; use inspect_load(...)
to surface expired lease warnings and active lease owner conflicts before
dispatch; the local Workspace backend
satisfies this seam for single-node development/restart recovery, but do not
describe it as a production cross-worker Redis/Postgres/object-storage
backend
third-party Workspace backends can replace the local backend through
agent.use_workspace(backend) or named provider registration with
Agently.workspace.register_backend_provider(name, factory) plus
agent.use_workspace(root, provider=name, provider_options={...}) when they
implement the Workspace backend protocol; current framework tests include a
protocol-level remote audit provider proof plus Workspace-backed snapshot
load tests for pause/continue, policy approval waits, and
when(..., mode="and") join progress, but public guidance must not imply
production Redis, Postgres, or object-storage support until real adapters and
operational guarantees exist
for AgentTask applications, enable only the bounded capabilities the task
may use, such as agent.enable_shell(...),
agent.enable_workspace_file_actions(...),
agent.enable_coding_agent_actions(...), agent.use_actions(...), or
agent.use_skills(...), then create the
task-strategy AgentExecution draft with agent.create_task(...) or the more
explicit agent.create_task_loop(...); both return AgentExecution drafts, not
public AgentTask handles. Do not expose broad shell, filesystem, MCP, or
browser access just because the task loop exists
when a running task-strategy AgentExecution needs additional optional context,
use execution.async_add_guidance(...) / execution.add_guidance(...).
This records guidance to the retained AgentTask Workspace and applies it at
the next safe Flat or TaskBoard boundary. Do not use guidance as completion
evidence, do not inject it into non-task route prompts, and use TriggerFlow
pause_for(...) / continue_with(...) instead when an external answer is
required before execution may continue.
when an AgentTask Skills step must perform side effects, grant the
relevant ActionRuntime tools/actions explicitly through route or effort
configuration, declare required side-effect actions when a Skills React loop
should stop after they succeed, and pair the task with structured
action_succeeded evidence requirements for those host Actions. A Skills
response that says it wrote or read an artifact is not execution evidence by
itself.
for AgentTask business examples, print or persist AgentExecution
stream/result/meta items: use meta.stream_kind=="snapshot" for intermediate
state captures such as context readiness, plan, execution evidence summary,
and verification gaps;
enable options={"agent_task": {"stream_progress": True}} only when
natural-language progress is needed; omit progress_model_key for template
progress with no model requests, or set progress_model_key to run a
separate background model that summarizes only existing snapshots/task
metadata without adding main-loop fields or latency; model-generated progress
streams progress_delta items before the final progress item; set
agent.language(...) when final output, process text, progress text, and
Action package locale defaults should share a language policy; Search derives
provider-specific region codes inside the Search package, while Browse may use
Accept-Language; use
progress_language per execution or agent_task.progress.language globally
only as progress-text compatibility controls when operator updates must be
Chinese, English, or another fixed language;
progress model failures are side-channel diagnostics/warnings, not main task
model.request_failed errors; progress model inputs should be operator-safe
and omit low-level Workspace/SQLite fallback diagnostics that remain
available in task meta; requirements, planner, and verifier model requests can
stream model_delta observation items with
validation_role=="observation_only" only when stream_model_stage_deltas or
a stage-specific override is explicitly enabled; use them for UI/log
visibility only, not for acceptance or verifier evidence; frontend consumers
should buffer/bucket high-frequency deltas or use smooth incremental rendering
to avoid render pressure and visible screen jitter
for AgentTask terminal results, treat completed as accepted output
(accepted=True, artifact_status="accepted"); explicitly configured
max_iterations can still leave useful Workspace files, but those are partial artifacts
(accepted=False, artifact_status="partial"); accepted degraded results use
artifact_status="degraded" and should include a user-facing
final_response that explains the disclosed unavailable/partial evidence
boundary; useful but unaccepted artifacts and blocked outcomes should also
include final_response so callers can show what was produced, where work
stopped, and which requirements remain unmet. Prefer final_response for
task-strategy get_text() display; use get_data() for the business
final_result view and get_full_data() for the complete route/task
envelope. When semantic content quality
matters, combine deterministic smoke checks with current docs/spec/source
references or an Agently model-judge request, and do not use counts, keyword
hits, or task verifier acceptance alone as the primary acceptance signal
when an AgentTask bounded step or TaskBoard card returns a short
artifact_markdown body or a sectioned artifact_manifest, the framework
writes the deliverable through the bound Workspace and reads it back for
path, bytes, sha256, preview, and trusted file_refs as cold
evidence; model-hot verifier input uses path/ref handles, bounded content or
preview, truncation status, and readback path handles rather than
SHA/byte/MIME integrity metadata; for long,
sectioned, or prose-heavy deliverables, choose the content carrier
deliberately: draft a single freeform document as natural Markdown/plain text
without .output(), or use Agently .output(..., format=...) with
xml_field, hybrid, or yaml_literal when separately addressable fields
are required instead of forcing the body into compact JSON fields; keep
status, evidence, and verification in separate compact judgment/readback
contracts; do not add .output() solely to trigger instant fields for the
body stream; use artifact_manifest.sections plus Workspace readback when
AgentTask must deliver a trusted file artifact;
AgentTask artifact writers consume AgentExecution stream facts: natural body
text comes from raw delta items, and retry boundaries come from $status when
the provider reports it. If the public type="delta""<$retry>...</$retry>" replay marker reaches the artifact consumer, treat
that exact marker as a retry control event; never write, clean into, or
transport it as artifact content;
if a complete Markdown artifact body appears inside structured evidence,
treat it as a deliverable body only when the evidence item is explicitly labeled
as artifact/body/deliverable/Markdown or tied to the manifest path; ordinary
source content and source excerpts remain evidence snippets. After trusted Workspace write/readback
succeeds, let terminal verification judge any stale artifact-write
remaining_work instead of planning another write-only step;
verifier-visible long Workspace artifacts may include bounded
workspace_artifact.targeted_readback ledger items from declared
output-contract sections and generic source/risk/reference/coverage anchors;
treat those as scoped evidence snippets, not completion judgments;
TaskBoard finalization keeps file-backed deliverable bodies in Workspace and
returns only a concise summary or path/ref pointer as final_result, not a
second copy of the file body;
host file-producing Actions should return typed file_refs or
artifact_refs when later AgentTask, TaskBoard, verifier, or UI consumers
need the produced file. A path-only payload such as {filename, path, size}
remains bounded Action result evidence and an external ref pointer; it is not
a trusted Workspace file unless the path is Workspace-contained and real
Workspace.read_file(...) succeeds, or the host returns explicit framework
refs;
model-declared file_refs remain diagnostics until readback exists; if write
succeeds but readback fails or lacks trusted fields, expect
agent_task.workspace_artifact.readback_failed or
agent_task.workspace_artifact.readback_insufficient diagnostics rather than
a generic budget or iteration failure
intermediate downloads, webpage snapshots, generated code, search notes, and
large extracted text may be persisted as Workspace/Action artifact refs and
opened later through bounded cold readback; hot prompts should carry compact
refs/previews, and these refs are execution evidence rather than final
deliverable proof. If Action artifact readback exposes Workspace file_refs
for a materialized download, TaskBoard readback promotes those nested refs to
card-level file_refs for later Workspace readback. If a non-final TaskBoard
card proposes a required final path such as final.md, AgentTask relocates it
to a working evidence path and reserves the final path for final synthesis
for large Workspace, repository, or file-backed evidence, AgentTask may
carry scoped retrieval query groups before broad reads. Flat uses
scoped_retrieval.query_groups; the Flat BlockCarrier lowers those groups to
pre-step Blocks workspace_operation.search facts. The operation name is
compatibility-stable, while execution uses Workspace retrieve(...) for
record/file candidates, optional vector/hybrid mode, structure-gated rerank
over a bounded candidate-summary window, refill, and budget packaging before
injecting a body-light model-hot evidence_ledger index plus
compatibility scoped_retrieval_results view into the bounded agent_step;
selected structured records may appear as compact projections with
projection/original_ref metadata so later readback can recover the raw
Workspace record;
reconstructable provenance such as SHA, byte counts, backend/search-engine
details, execution block ids, and full file refs stays in raw
Workspace/Blocks evidence for programmatic audit and readback. TaskBoard
exposes the same work-unit carrier contract and can inject ledger-backed retrieval facts
into card execution. TaskBoard Workspace-operation prompt views and
available readback handles, readback work-unit hot payloads, Action artifact readback previews, and
intermediate Workspace readback previews use the same hot/cold split:
content/path, range, truncation, and compact handles stay hot, while SHA,
bytes, handler/media, backend/search-engine facts, execution block ids, and
full refs stay in cold evidence, final artifact audit metadata, DevTools, or
runner logs. SHA is integrity metadata, not source evidence. Query groups may
set search_surface to workspace_index, workspace_files, or
workspace_index_and_files; record collections belong in
filters.collection, exact record kinds may use filters.kind, and file
scopes use top-level path/pattern; that file path is not applied as a
record filter. Singleton record filter lists are normalized to
scalar values before execution so retained Workspace records and files can
stay out of hot context until bounded search/readback needs them.
For workspace_files, query is content text, path is the directory/file
scope, and pattern is a file glob such as *.md, *, or ** for recursive
file search, not another content keyword. Local Workspace file search uses
rg as a grep-style search engine when available and falls back to bounded
file scanning. Blocks return a small bounded context around file matches by
default, so nearby facts can be inspected without reading the whole file.
EvidenceEnvelope.evidence_items records search success, empty result,
failure, locator, snippet, and readback facts with stable ids, status,
body_state, and model-hot cite_as handles. Failed/empty items support
unavailable or missing-data claims only; ref_only items support only
discovery until readback exists; snippet facts expose whether bounded context
was truncated. Structured outputs may include evidence_use claim bindings
to cite_as or canonical ids; path/URL/record/artifact/action aliases are
canonicalized only when unambiguous.
Ordinary intermediate Flat work units use non-empty remaining_work to make
the next Flat iteration consume the new evidence by default instead of
triggering an immediate independent verifier. They may also return
ready_for_final_verification=false to make that intent explicit, while
explicit ready_for_final_verification=true is reserved for terminal,
blocking, or risk verification now.
Ordinary intermediate TaskBoard cards should let downstream consumer cards
decide whether evidence is enough. Independent verifier requests are for
terminal acceptance, fan-in/control acceptance, evidence/artifact boundary
audit, contradictions, or high-risk review.
When terminal verification fails, compact repair_context is carried into
the next Flat work unit and Workspace artifact draft request when present, so
the consumer that actually rewrites or reads the artifact receives the latest
acceptance_delta, repair constraints, next-step requirements, and exact
evidence anchors without reintroducing cold integrity provenance into hot
prompts.
Workspace retrieve(...) plus grep_files/search_files, and Blocks
workspace_operation.search/read_bounded, return factual locator_ref and
evidence_snippet records; selected structured record snippets may be
compact projections that preserve projection/original_ref metadata for
raw Workspace readback; model-owned
planning/verification decides whether snippets are useful. If a TaskBoard
scoped-retrieval card reports blocked/insufficient output without an explicit
next action, AgentTask synthesizes an expanded evidence card plus a
continuation card so the downstream consumer can decide whether the new
evidence is enough. Do not turn local grep/SQLite hits into semantic
relevance gates, quality checks, or completion evidence
TaskBoard readback cards may inspect both Action artifact refs and trusted
Workspace file/content refs through bounded cold readbacks. Framework-generated
readback cards scope evidence to direct dependencies plus upstream evidence
cards, so a control-card readback can still inspect Action refs produced by
earlier evidence-gathering cards; generated continuation cards should not
recursively request another identical readback chain when the same evidence
remains insufficient. When the missing evidence is a new concrete URL, path,
or ref, the control card should return structured target_refs with
next_board_action=readback; external HTTP/HTTPS refs become Action evidence
work, while Workspace/content paths and retained-note refs become bounded
Workspace readback cards. Prose-only gaps are diagnostics, not executable targets
completed and sufficient TaskBoard control outputs may still disclose
non-fatal gaps; those gaps do not block Workspace artifact materialization,
while remaining_work, blocked status, repair, or readback intent still do.
Materializing an artifact creates readback/verification evidence and is not
final task acceptance
AgentTask strategy persistence writes planning, observation, verification,
checkpoint, and evidence-link records through the bound Workspace provider;
checkpoints use the checkpoint-store port and task evidence relationships use
workspace.link_evidence(...). TaskBoard checkpoint payloads may also carry
bounded acceptance-index and handoff projections for resume orientation; they
point back to TaskBoard revision, EvidenceEnvelope, artifact, and checkpoint
refs, and must not be treated as a second evidence ledger or completion
verdict. Acceptance-index projections may include dirty/cache state, verdict
fingerprints, scoped evidence refs, and progress counters so TaskBoard can
skip redundant verifier work for unchanged green criteria while still
verifying dirty items and required host guards
for AgentTask business-system examples, mocks may provide facts, source
records, policies, missing data, or conflicting inputs, but must not provide
hidden expected answers, pass/fail fields, or deterministic business-quality
verdicts; judgment belongs to the AgentTask verifier or an independent
Agently model-judge request
for app developers, prefer agent.enable_python(...), agent.enable_shell(...), agent.enable_workspace_file_actions(...), agent.enable_coding_agent_actions(...), agent.enable_nodejs(...), agent.enable_code_runtime(...), and agent.enable_sqlite(...) before direct manager/provider APIs; Python, shell, Node.js, and common-language code runtime helpers use Docker-backed runtime profiles and fail closed when Docker preflight fails. Strict profiles report missing images, while developer/CI profiles may pull missing images and prepare standard dependencies as host-owned provisioning. Use sandbox="trusted_local" only for trusted compatibility paths. Shell is for tests, builds, git inspection, and read-only diagnostics, while Workspace file actions own file read/search/edit/write semantics
for instruction-heavy or large Actions, expect later model rounds to see compact execution digests, bounded previews, artifact refs, and file refs; if the digest is still too large for planning or reply hot paths, ActionRuntime may replace duplicate data/model_digest fields with same_as=result pointers and omit artifact preview bodies from hot-path refs; previews are not complete evidence, so use agent.action.read_action_artifact(...) only when full raw code, command output, SQL results, page content, or logs are needed
treat model-planned Action inputs as untrusted: ActionDispatcher filters
structured_plan and native_tool_calls kwargs to registered
ActionSpec.kwargs, records stripped keys in ActionResult.diagnostics, and
leaves direct host calls compatible
agent.action.get_action_info() and get_action_list() return visible
metadata with execution environment env values redacted; raw env values are
only for the execution provider path
after explicit agent.get_action_result(prompt=..., store_for_reply=True),
the prompt is marked as having consumed the action loop even when no records
were produced, so later response materialization should not re-enter
ActionRuntime for that prompt
agent.get_action_result(..., timeout=N) bounds the full ActionFlow
lifecycle, including planning/model-owned tool selection; catch
RuntimeStageStallError(stage="action_loop_close") instead of relying on a
host-side kill as the primary timeout signal
for native tool-call planning, a zero-tool-call result can surface as a
skipped diagnostic record with code action_runtime.native_tool_calls.empty;
treat it as planning evidence, not executed work
use agent.action.summarize_records(records, validation_command_markers=...)
when a host needs authoritative action evidence such as failed actions,
commands attempted/run, and latest validation result
treat Agently.execution_resource as an advanced framework/plugin surface for lifecycle, policy, approval, health, and release
Action executors should declare or consume managed resources instead of secretly owning long-lived MCP clients, sandboxes, browsers, SQLite connections, or process runners
treat agently-devtools as optional PyPI-installed tooling; wire observation through public bridge APIs, not source-repo paths
for framework internals, preserve Agently's core module style: class-owned
runtime behavior, typed data contracts, protocol/handler seams for
replacement, and high-level packages outside agently/core when they compose
several core systems
official Agently RuntimeEvent records are core-owned: built-in plugins should
return typed observations, errors, decisions, or route stream facts to core
rather than importing core RuntimeEvent emitters; plugin-owned custom Event
Center messages must use plugin-owned namespaces and are not guaranteed as
framework consumption contracts
keep runtime naming aligned with DevTools: agent_execution is the canonical
run lineage kind for one bounded Agent execution. The old turn-named runtime
namespace is removed from the current development-line contract.
attempt_index is model-request retry metadata and must not be treated as an
AgentExecution counter. AgentExecution process streams are observed as
agent_execution.stream RuntimeEvents for flat and TaskBoard execution
progress; DevTools may display those facts but must not own task strategy
selection.
for framework-side Skills work, treat SkillsManager as an internal
capability-adapter owner, not a new public recommended facade. Internal code
should depend on SkillsManager; user-facing examples should prefer
agent.use_skills(...) and AgentExecution Skills selection. Use legacy
Agently.skills_executor only when a compatibility registry/resolver hook is
required. Context-pack calls are advanced custom-planner/TaskDAG integration
surfaces, not the ordinary user path.
for Agently 4.1.3 Skills runtime work, prefer Action-like management:
declare installed ids or remote source selectors on agent.use_skills(...)
and let Skills Manager lazily discover, install, and expose selected
capability needs/candidates; AgentExecution owns run-local mounting,
execution, and cleanup. Keep install_skills_pack(...) for prewarming, offline mirrors,
deterministic CI fixtures, and explicit registry maintenance
Skills installs default to the user-global registry ~/.agently/skills;
override skills.registry.root only for isolation, and use
skills.registry.proxy or runtime.network.proxy when remote pack fetches
need a configured proxy
use install_skills(...) for one local Skill directory during
authoring/smoke tests; use AgentExecution with execution.require_skills(...)
for explicit required Skill activation; remote selectors may use git URLs,
GitHub shorthand such as
anthropics/skills, and subpath= when selecting one Skill from a pack
Agently.skills_executor and agent.run_skills_task(...) are pre-4.2
compatibility facades only. They still lower direct Skills execution through
the current Blocks runtime, but new examples and app guidance should use
Agent APIs and AgentExecution Skills selection instead.
when a custom planner, Dynamic Task, or TaskDAG node needs full Skill context
without forcing Skill activation execution, use
agent.build_skills_context_pack(...),
agent.async_build_skills_context_pack(...), or
the legacy Agently.skills_executor.build_context_pack(...) facade; the returned
agently.skills.context_pack.v1 payload can include SKILL.md guidance,
task-relevant references/examples/assets, citations, diagnostics, optional
public lookup, and policy-gated script Action candidates
when a planner needs progressive Skill context before lowering a Task to a
skill_activation ExecutionBlock,
use the internal SkillsManager adapter or the legacy
Agently.skills_executor.activate_skill(...) compatibility facade. Treat the
returned SkillActivation as
skill-context evidence and capability need discovery only; it does not execute
bundled resources, grant Actions/MCP/shell/browser access, or prove side
effects
for DAG-shaped consumers of Skill context, pass
Agently.skills_executor.task_dag_resolver() to TaskDAGExecutor and use
kind="skill" nodes; do not build a separate scheduler or execute bundled
scripts while constructing context packs
for explicit required Skills, create an AgentExecution draft, set prompt and
output contracts on that draft, then call execution.require_skills([...]).
Required multiple Skills are preserved in caller order and lowered to ordered
TaskGraph Tasks plus ordered skill_activation ExecutionBlocks; later Skills
receive compact previous Skill results as evidence
when Skills are reached through Agent auto-orchestration, pass
execution-block-owned effort with
agent.create_execution(options=ExecutionOptions(execution_blocks={ "skill_activation": SkillActivationOptions(effort="normal")})) or the
equivalent dict;
this is an execution option, not a prompt slot
do not add application-specific Skills execution strategies through
framework-level strategy registration. Model requests, ActionRuntime/MCP,
ExecutionResource, TriggerFlow, Dynamic Task, and host policy should be
composed by AgentExecution planning/lowering instead of a parallel
Skills-owned dispatcher
Skill activation stream handlers receive Skills runtime item dictionaries and
can be annotated with
SkillRuntimeStreamHandler; model stream handlers passed to
context.async_request_model(..., stream_handler=...) receive StreamingData
and can be annotated with ModelStreamingHandler; the common aliases are
available from agently
for MCP, prefer Streamable HTTP URLs for service integrations
(agent.use_mcp("https://host/mcp")), use headers= for URL auth, and use
MCP config dictionaries for stdio/multi-server local integrations; treat SSE
as a legacy compatibility transport
treat chained Agent quick prompt methods as AgentExecution-local configuration
for one request/execution surface. Ordinary
agent.input(...).output(...).start() expressions create a fresh execution
for that expression, including inside loops. For multi-statement execution
setup, use execution = agent.create_execution() and mutate the execution
before it starts, not the shared Agent pending prompt. A completed explicit
AgentExecution is an immutable run record; prompt/config mutators should
fail fast on that object, and the next request should use a new execution.
semantic_outputs= is only a deprecated compatibility alias for direct
Skills execution, while Dynamic Task still uses semantic_outputs inside
TaskDAG specs
for framework-side Skills execution, keep standard SKILL.md as the only
capability definition; selected Skills default to a single_shot
compatibility label that lowers to handler-backed model-request execution,
while staged/react/custom labels lower to handler-backed execution segments and
should compose TriggerFlow and ActionFlow/ActionRuntime rather than adding a
Skills-local executor
for Skills react tool use, delegate model-owned action planning and
execution to the Agent ActionRuntime so action/MCP kwargs schemas, policy,
approvals, concurrency, and managed resources stay on the Action layer
when Skills react delegates to ActionRuntime, keep the selected SKILL.md
guidance/resource context in the ActionRuntime planning prompt. Delegation
with only task text and observation history bypasses the Skill guidance.
when Skills react, AgentTask, or another higher-level runtime delegates
action planning to ActionRuntime, set action.planning_model_key to the
intended model_pool business key so action planning uses the same model
routing as the surrounding task
Skills are public-standard SKILL.md guidance/resources, not Agently-private
capability manifests; do not author or recommend allowed-actions,
allow-scripts, mcp, mcpServers, execution, or stages frontmatter as
Agently capability declarations
SkillsManager discovers selected Skill capability needs from SKILL.md,
resource indexes, public compatibility, public metadata, and optional
model-assisted inference, then records structured capability_needs; selected
Skills may guide the model to use capabilities when available, but they do not
grant them
host applications control Skills capability loading with
agent.configure_skill_capabilities(auto_load={...}); allow can auto-load
built-in Search/Browse/HTTP/Workspace/Python/shell-script/MCP capabilities,
while approval and off fail closed with diagnostics instead of silently
mounting tools
with capability_scope="execution", AgentExecution releases only capabilities
newly mounted for that execution; if the host Agent already owns the requested
action id, reuse that action and leave it registered after the Skills run
for search-oriented Skills, prefer the framework Search package backed by the
ddgs Python package; keep ddgs upgraded with
python -m pip install --upgrade ddgs before real search runs, and leave the
ddgs backend strategy configurable (auto by default) instead of hard-coding
a single backend; Search treats backend-level "no results" as an empty
successful action result and falls back through configured/default ddgs
backends when a selected backend returns no usable parsed result; if fallback
recovers after backend failures, Search reports status="partial_success"
with success=True and diagnostics rather than an action.failed terminal
condition
when observing ActionFlow records, treat action.approval_required and
action.blocked as policy/sandbox gate outcomes, not ordinary
action.failed execution failures
Workspace owns file-action roots and path boundaries; expose file access
through Workspace-scoped file actions, and treat ActionRuntime as the callable
surface rather than the owner of Workspace semantics, file type parsing, or
export rendering
Skills capability approval is resolved through Agently's global
PolicyApproval handler, not a SkillsManager-local handler:
input_timeout_fail by default, auto_approve for tests/trusted local
profiles, fail_closed when a host wants pending diagnostics or TriggerFlow
policy interrupts, input for local CLI interruption, or a host-provided
durable/network handler
trusted hosts may grant automatic approval through settings-backed
access_control_policy.auto_allow, using
Agently.set_settings("access_control_policy.auto_allow", True),
agent.set_settings("access_control_policy.auto_allow", True), execution
.access_control_policy({"auto_allow": True}), or selector-level
auto_allow=True for a matching Skill. Do not introduce
configure_access_control_policy(...); if a wrapper is ever added, it must
delegate to settings and own no independent state
production services should choose a PolicyApproval handler that matches the
service wrapping the TriggerFlow execution: database pending record, HTTP
callback, webhook resume, SSE/WebSocket pending stream, save-and-return
interrupt id, or another host-owned approval channel; tests may use
auto_approve
script resources under scripts/ can be wrapped as scoped shell actions only
when host policy allows script_run or a host-owned auto_allow grant applies;
selector-level auto_allow is scoped to matching Skill ids. The action root must
be the installed Skill directory and execution still goes through ActionRuntime
/ ExecutionResource boundaries
public Agent Skills allowed-tools is experimental and, if supported, can
only restrict or pre-approve already-mounted host tools; it must not create
tools, synthesize backends, mount MCP, or choose Skills execution strategy by
itself
for Agently auto-orchestration work, treat
agent.use_skills(...).input(...).get_result() as capability-candidate
registration when the caller needs reusable data/text/meta/stream readers;
this is owned by the AgentExecution planner, not prompt-only Skills guidance
injection by default
for ambiguous optional capability candidates, keep submitted Dynamic Task and
required Skills deterministic, but let the model choose among optional auto
Dynamic Task, model-decision Skills, and ordinary Action-backed model request
Tasks
for framework-side Skills, treat standard SKILL.md as the only capability
definition; Agently install metadata and decision cards are descriptive
runtime aids, not authoring formats or availability gates
for Skills actions, use the global PolicyApproval contract plus
Action/ExecutionResource boundaries; when the request is
orchestrated, pending approvals must become TriggerFlow policy_approval
interrupts instead of Skills snapshots
keep Agent auto-orchestration behind the AgentOrchestrator plugin protocol:
core owns the public agent.create_execution() entrypoint, while the active
plugin owns Task planning, execution lowering, and stream bridging
for unified Agent execution/result work, prefer a response-style
agent.create_execution() object with data/text/meta/stream consumption; use
TriggerFlow runtime stream plus ModelRequest instant checkpoints for process
streaming, and expose model field deltas only through stable structured paths
such as task_dag.tasks.<task_id>.fields.<field_path> rather than raw provider
token events
for bounded developer-owned loops, use
agent.create_execution(lineage=..., limits=..., options=...); lineage,
diagnostics, stream correlation metadata, and shared model-request budget
counting now describe the bounded step without a public mode name
keep AgentExecution memory explicit: write observations/checkpoints/artifacts
through the execution's bound Workspace helper, such as
await execution.async_record_workspace(collection="observations", checkpoint=True),
which writes requested checkpoints through the Workspace checkpoint-store port
and records an evidence link between the AgentExecution record and checkpoint;
then call workspace.build_context(...) for the next step; ordinary one-turn
AgentExecution remains explicit, while AgentTask owns its strategy-level
persistence; do not make Workspace depend on AgentExecution-specific strategy
semantics
inspect AgentExecution runtime facts through AgentExecutionResult or the
execution facade: result = execution.get_result(), result.get_text(),
result.get_data(), result.get_full_data(), result.get_meta(),
execution.get_prompt_text(), execution.get_data_object(),
execution.get_key_result(...), execution.wait_keys(...),
execution.get_async_generator(), execution.streaming_print(), and
await execution.async_get_meta().
meta["task_frame"],
meta["task_graph"], and meta["execution_blocks"] record the selected Task
structure and concrete execution lowering, while meta["logs"] exposes model
response ids, ActionRuntime action logs, task refs, and artifact refs when available; use those
framework-owned records for Workspace persistence instead of asking the model
to copy raw action stdout into final text. type="delta" remains the public
string stream; type="instant" yields original structured items and appends
synthetic path="$delta"AgentExecutionStreamData items only as a consumer
text projection when a source item can be projected to natural language;
type="all" remains the raw audit stream and must not include synthetic
$delta items. Rich task UIs should consume instant for structured state
and render public/synthetic $delta only as visible process text; public
delta is a printable compatibility stream, not the UI state owner
when AgentExecution planning selects direct model_request, treat
Action and Observation as skipped business stages and consume the model result
as passthrough. If Action or Skill candidates are available but the selected
Task remains direct model_request, they are only available capabilities, not
a planned Action/Skill task. Only actual action/tool records become
side-effect evidence for verification, Workspace persistence, or replan.
set limits={"max_seconds": ..., "max_no_progress_seconds": ...} only when
the host explicitly needs hard wall-clock or no-progress controls while
diagnosing or building host-owned loops; framework defaults should not impose
model-request, iteration, TaskBoard tick, Action round, node-count, or
tool-call quotas, while no-progress and idle timeouts remain liveness guards;
catch RuntimeStageStallError from the root agently.core export or
agently.core.application.AgentExecution and inspect
meta["diagnostics"]["last_progress"], ["timeouts"], and ["stalls"]
instead of adding ad hoc polling around the whole app
for provider stream hangs, prefer framework settings such as
OpenAICompatible.stream_idle_timeout,
OpenAIResponsesCompatible.stream_idle_timeout, and
AnthropicCompatible.stream_idle_timeout, plus
response.materialization_idle_timeout; use None for unlimited budgets and
avoid permanent debug-only timeout wrappers in examples; provider first-event
and stream-idle waits should surface as RuntimeStageStallError with
stage="response_first_event" or stage="response_stream"; transient
disconnects use same-request <Requester>.request_retry; OpenAICompatible
replays after partial output by default, which requires streaming consumers
to process $status or clear plain-delta text on the
"<$retry>{reason}</$retry>" marker before accepting replacement deltas;
the marker belongs to public delta replay and must not be treated as internal
artifact content;
explicit response stream errors should propagate from response getters with the
original provider or ActionFlow reason before materialization timeout is used
as a fallback
when high-frequency RuntimeEvent deltas would overload downstream consumers,
keep producers raw and ask the expensive EventCenter outlet to summarize with
hook/hooker delivery_policy={"mode": "summary", "dispatch": "await", "emit_interval": ..., "max_items": ...}; use dispatch="background" only
for best-effort outlets that call EventCenter.async_flush(...) or expose an
owning bridge flush/close point; EventCenter also has an idle flush safety net
for long-lived loops, but CLI/script shutdown still needs explicit flush for
background outlets; rely on AgentExecution liveness diagnostics rather than
public delta frequency for stall detection
for temporary development debugging, attach an EventCenter observation hook,
call .set_settings("debug", True) for request/result and AgentTask process
summaries, or call .set_settings("debug", "detail") for full observation
detail including model delta output;
remove debug hooks/settings from examples and production snippets after the
issue is diagnosed
Anti-Patterns
do not build a parallel action/tool dispatcher before checking Action Runtime
do not hand-roll tool schema prompts or kwargs planners in Skills when the
Agent ActionRuntime can plan against the registered action list
do not accept prompt-only or React Skills text as proof of file writes,
readbacks, shell execution, HTTP calls, or other side effects; require
ActionRuntime records and structured evidence
do not expose core ExecutionResource manager APIs as the default app-development mental model
do not route package installation or filesystem mutation through the Python sandbox
do not ask users to clone or editable-install DevTools when pip install agently-devtools fits
do not make DevTools the source of truth for workflow structure
do not present prompt-only Skills disclosure as the default execution meaning
of agent.start() once the 4.1.3 AgentExecution planner owns Skills candidates
do not put TaskGraph/ExecutionBlock-owned Skills, Dynamic Task, or stream-composition logic
directly in core when a plugin protocol can own the replaceable behavior
