Integrating Hosted Research Agents into Applications

Calling a language model usually looks synchronous: send a prompt, wait for a response, and display the text. A hosted research agent such as Manus has a different execution model. It may browse the web, use connectors, work with files, run tools in a sandbox, pause for clarification, and produce several artifacts over minutes rather than seconds.

That difference changes the application architecture. The integration is no longer only an API request. It is a long-running job whose identity, state, inputs, approvals, outputs, and failures must be preserved.

This page uses Manus as the concrete example. It explains how the hosted agent works, the different ways to use it, how to integrate it into an application, and how its credit-based pricing differs from calling a model API directly. The architectural patterns also apply to other hosted agent services.

Version note: Manus changes quickly. The API details and prices on this page were checked against official documentation on July 22, 2026. API v1 is deprecated. Verify version-sensitive fields, limits, agent profiles, event types, and prices before implementing them.

The Starting Mental Model

A normal model call asks a model to generate the next response. A hosted agent task delegates a goal to an execution system.

Direct model call	Hosted agent task
Usually one request and one response	Asynchronous task with several events
Your code explicitly calls every tool	The agent can select and sequence available tools
Usually completes in seconds	May run for minutes or longer
Returns text or a structured response	May return messages, files, links, and structured data
Your process owns the complete loop	The provider runs part of the loop in its own environment
Missing information usually produces a best-effort answer	The task may stop and ask a question

The useful abstraction is therefore:

Create task -> persist task ID -> observe events -> supply input if needed -> deliver result

Do not keep the original HTTP request open while the agent works. Return a local job ID or acknowledgement to the caller, then continue the task asynchronously.

What Manus Adds Beyond An LLM

Manus describes its product as a general-purpose autonomous agent with access to a cloud execution environment. Through its web product and API, a task can use files, web browsing, connectors, skills, and other tools while working toward an outcome.

That makes Manus closer to a managed agent runtime than a raw inference endpoint:

flowchart LR
    U["User goal"] --> M["Manus task"]
    M --> P["Plan and choose actions"]
    P --> B["Browse or search"]
    P --> F["Read and create files"]
    P --> C["Use approved connectors"]
    P --> S["Run skills or tools"]
    B --> R["Research result"]
    F --> R
    C --> R
    S --> R

The application does not need to implement every research step. It still owns everything around the delegated task:

authenticating the user
deciding what context and permissions may be sent
recording the relationship between the user request and the Manus task
presenting clarification and approval requests to the correct person
verifying callbacks
enforcing application-specific policy
storing and delivering the final result
handling cancellation, retries, and audit records

Manus can persist progress inside its task runtime. That does not make the rest of your application durable automatically.

Ways To Use Manus

Not every use case needs a custom API integration. Manus exposes its agent through several entry points. Choose one based on where the request begins, who needs to participate, and where the result must go.

Method	How it works	Best fit
Web or mobile app	A person gives Manus a goal, follows progress, answers questions, and reviews the result	One-off research, analysis, file creation, websites, and testing a task before automating it
Desktop app with My Computer	Manus can work with approved local folders and command-line tools; local commands require user authorization	Local files, development environments, scripts, and workloads that need the user’s computer
Projects and scheduled tasks	Projects supply recurring instructions and files; schedules start tasks automatically	Repeated reports, monitoring, and work that needs a stable knowledge base
Slack	A user tags `@manus` in a thread; Manus reads that thread’s context and returns progress and results there	Collaborative team work that should stay inside Slack
Mail Manus	Approved senders forward or copy an email to a Manus address; Manus processes the message and attachments and replies by email	Inbox-driven work such as invoice extraction, travel planning, and recurring email workflows
Zapier	A no-code trigger in another application creates a Manus task, then later steps use the result	Business automation without maintaining integration code
API v2 and webhooks	Server code creates and manages tasks, uploads files, receives events, and routes results into a product	Custom applications that need their own authorization, data model, interface, audit trail, or delivery logic
Open App OAuth	A third-party application acts for an authorized Manus Team user with scoped access	Multi-user products that should not share one account-wide API key

These methods are entry points, not separate agent engines. A task can still use Manus capabilities such as its cloud browser, files, data sources, skills, approved connectors, MCP servers, and structured output.

The current API is v2. It documents the manus-1.6, manus-1.6-lite, and manus-1.6-max agent profiles, uses lifecycle states such as running, waiting, stopped, and error, and supports both polling and signed webhooks. Projects, pre-uploaded files, connectors, skills, and output schemas provide reusable context without placing everything in one prompt.

A Production Reference Architecture

The thinnest safe integration has two paths:

A command path creates or continues a task.
An event path receives status changes and routes the result back.

flowchart TB
    U["User in web app, Slack,<br/>email, or internal system"] --> I["Application intake<br/>auth, validation, policy"]
    I --> DB[("Local job and<br/>correlation store")]
    I --> Q["Application queue"]
    Q --> MC["Manus API client"]
    MC --> MT["Manus task runtime"]
    MT --> SB["Cloud sandbox"]
    MT --> CN["Approved connectors,<br/>skills, files, and browser"]
    MT --> WH["Signed webhook"]
    WH --> WR["Webhook receiver<br/>verify, deduplicate, acknowledge"]
    WR --> EQ["Event queue"]
    EQ --> DB
    EQ --> O["Output adapter"]
    O --> U

The database is not merely a cache. It is the application-side source of truth for who requested the work, where replies belong, which permissions were approved, and which events have already been processed.

Persist Correlation Before Returning

When task.create succeeds, Manus returns a task_id. Store it before acknowledging the request to the user.

A useful local record contains:

Field	Why it matters
`local_job_id`	Stable identifier exposed by your application
`provider` and `provider_task_id`	Routes events to the correct external task
`requesting_user_id` and `tenant_id`	Preserves ownership and tenant isolation
`source_type`	Web, Slack, email, scheduled job, or another channel
`source_conversation_id`	Routes questions and results to the original conversation
`source_message_id`	Supports threading and deduplication
`status`	Your normalized application state
`provider_status`	Raw provider state for debugging
`policy_snapshot`	Records the permissions and limits used when the task began
`created_at`, `updated_at`, `expires_at`	Supports timeout and cleanup behavior
`last_event_id` or processed event table	Prevents duplicate webhook effects
`result_manifest`	Records messages, structured data, and copied artifacts

Use a unique constraint on (provider, provider_task_id). Store webhook event IDs in a separate table with a uniqueness constraint if more than one event can be processed per task.

For Slack, the mapping often looks like:

(workspace_id, channel_id, thread_ts) <-> local_job_id <-> manus_task_id

That relationship is what turns a provider callback into a reply in the correct Slack thread. Do not ask the model to reconstruct routing metadata from the conversation.

Creating A Current Manus Task

The current v2 endpoint is asynchronous. The API response confirms creation; it is not the research result.

const response = await fetch("https://api.manus.ai/v2/task.create", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    "x-manus-api-key": process.env.MANUS_API_KEY!,
  },
  body: JSON.stringify({
    title: "Research payment orchestration options",
    interactive_mode: true,
    share_visibility: "private",
    agent_profile: "manus-1.6",
    message: {
      content: [
        {
          type: "text",
          text: [
            "Compare three approaches for durable payment orchestration.",
            "Use primary sources, distinguish facts from recommendations,",
            "and return links for every material claim.",
          ].join(" "),
        },
      ],
      connectors: ["connector-approved-by-policy"],
    },
  }),
});

if (!response.ok) throw new Error(`Manus returned ${response.status}`);

const created = await response.json();
await jobs.attachProviderTask(localJobId, created.task_id);

The identifiers in connectors are account-specific connector IDs, not connector names. Resolve them from the current connector inventory and allow only those approved for the tenant and use case.

After persisting the mapping, your own API can return something like:

{
  "job_id": "job_7f82",
  "status": "accepted"
}

The client can show progress without knowing the Manus API key or provider task ID.

Treat The Task As A State Machine

Application state should be explicit. Do not infer completion from the absence of recent messages.

stateDiagram-v2
    [*] --> Accepted
    Accepted --> Running: task created
    Running --> WaitingForInput: clarification requested
    Running --> WaitingForApproval: action confirmation requested
    WaitingForInput --> Running: user message sent
    WaitingForApproval --> Running: action confirmed
    Running --> Completed: stopped with finish
    Running --> Failed: error
    Running --> Cancelled: stop requested
    WaitingForInput --> Expired: input deadline reached
    WaitingForApproval --> Expired: approval deadline reached
    Completed --> [*]
    Failed --> [*]
    Cancelled --> [*]
    Expired --> [*]

Manus v2 reports provider states through status_update events:

Provider state	Meaning	Application action
`running`	The agent is still working	Record progress; do not declare success
`waiting`	The task needs a message or action decision	Inspect `waiting_for_event_type`
`stopped`	The task reached a stopping point	Read results and determine the stop reason
`error`	The task failed	Store error detail and apply retry or escalation policy

The webhook layer uses a related distinction: a task_stopped event can have stop_reason: "finish" or stop_reason: "ask". A stopped webhook therefore does not always mean final completion.

Keep provider state and local state separate. Your application may classify a technically finished task as delivery_failed if the result could not be stored or posted back to the user.

Polling And Webhooks Serve Different Needs

Polling

Polling task.listMessages is useful for:

prototypes and command-line tools
manual troubleshooting
reconciliation after a missed callback
interfaces that deliberately refresh status on demand

Polling too frequently wastes quota and creates avoidable latency and load. Use exponential backoff with jitter, stop at a defined deadline, and preserve the last event or cursor you processed.

Webhooks

Webhooks are better for production completion and input notifications because the application does not need to repeatedly ask whether a task changed.

A production handler should:

Read the raw request body.
Verify X-Webhook-Signature and X-Webhook-Timestamp.
Reject stale timestamps.
Parse the event only after verification.
Insert event_id into a deduplication table.
Enqueue processing.
Return HTTP 200 quickly.

Manus currently signs the string below with RSA-SHA256:

{timestamp}.{full_webhook_url}.{sha256_hex(raw_body)}

The official guidance rejects requests more than five minutes from the current time and expects webhook endpoints to respond within ten seconds. Fetch the public key from webhook.publicKey, cache it, and support key refresh. Do not fetch the key for every callback.

Webhook delivery should be at least once from the application’s point of view. Even if a provider usually sends one callback, network retries can produce duplicates. Make state transitions and downstream notifications idempotent.

Also run a periodic reconciliation job. It can query nonterminal jobs that have not received an event recently. Webhooks reduce polling; they should not be the only recovery mechanism.

Clarification Is Not Approval

The waiting state has two different meanings that must not be collapsed.

Clarification

When waiting_for_event_type is messageAskUser, the agent needs additional information. Show the question to the user and continue the same task with task.sendMessage.

Examples:

Which market should the report cover?
Should the comparison use current pricing or historical pricing?
Which of these ambiguous entities did the user mean?

The reply should be routed from the same authenticated user or another actor explicitly authorized to continue that task.

Action confirmation

Other waiting event types represent proposed actions. Use task.confirmAction and validate its confirm_input_schema rather than assuming every event accepts the same payload.

Examples include sending email, changing calendar data, deploying an application, executing a terminal command, or selecting a browser session. New event types may be added, so unknown types should fail closed and enter a manual-review path.

An approval screen should show:

the exact action
target resource and account
important parameters
expected side effect
which user and agent initiated it
when the approval expires

Approval must be bound to the displayed parameters. If the action changes, obtain a new approval. See Agent Identity and Delegated Access for the full authorization model.

Context Is A Designed Input Surface

A common integration mistake is to send every available file, connector, and conversation message “just in case.” More context is not automatically better. It increases cost, expands the security boundary, and can make the agent follow irrelevant or malicious instructions.

Choose the narrowest suitable context mechanism:

Mechanism	Use it for	Important boundary
Task prompt	Goal, scope, output criteria, and current request	Keep secrets and routing metadata out
Inline file URL or base64	Small task-specific input	Current v2 limit is 20 MB for these paths
Pre-uploaded `file_id`	Larger task-specific files	Current upload path supports files up to 512 MB
Project	Repeated work with shared instructions and reference files	Project context applies to every task in that project
Connector	Controlled access to an external account or data source	Pass an explicit approved connector allowlist
Skill	Reusable procedure or domain behavior	Treat skill instructions as versioned code or policy
Browser	Work requiring an authenticated or interactive browser	Browser access can expose session data and enable side effects
Structured-output schema	Machine-readable result contract	Extraction happens after the agent run; it does not constrain every action

Projects Are Scoped Memory, Not Universal Memory

Projects apply shared instructions to related tasks. Manus also documents a newer feature that can review a completed conversation and propose updates to Project instructions, files, or skills. A person approves the changes before they affect later tasks.

This is useful organizational memory, but it should not be described as an agent remembering everything:

the knowledge is scoped to a Project
proposed updates require authorization
only selected reusable information is promoted
source files and instructions can still become stale
sensitive or temporary details should not automatically become permanent context

Treat Project updates like documentation changes: review them, preserve provenance, and remove obsolete rules.

Connectors Need Explicit Selection

If connectors are omitted, Manus can resolve defaults from the Project or account. That is convenient in the interactive product but risky in a multi-tenant or policy-sensitive integration.

Prefer to resolve and pass a connector list explicitly. The application should decide which connector may be used for this user, tenant, and task. OAuth authorization proves that an account was connected; it does not prove that every task should access it.

Do Not Pass Credentials In Prompts

API keys, refresh tokens, session cookies, and database passwords should stay in trusted infrastructure. Use connector authorization, server-side secret stores, or a narrowly scoped tool boundary. Do not serialize credentials into prompts, files, agent memory, or logs.

Authentication Depends On Who Owns The Integration

Manus v2 supports two main authentication patterns:

Pattern	Appropriate use
API key in `x-manus-api-key`	Your own backend, internal scripts, or a single-account integration
OAuth bearer token	A third-party application acting for different Manus users

An API key has broad access to its account and belongs only on the server. Store it in a secret manager, rotate it, and never expose it to a browser or Slack client.

For a product used by external customers, use the Manus Open App OAuth flow and request only necessary scopes. A token with the narrower create_task scope is limited to tasks created by that Open App. Broader task-management, connector, project, and browser scopes should be requested only when the product actually needs them.

The same principle applies inside your application: the Manus credential identifies the integration, while your own authorization layer determines which local user may create, read, continue, approve, cancel, or share a particular task.

Keep task visibility private unless sharing is an intentional product feature. A public share URL is a data-release decision, not a presentation preference.

Design The Output For Its Consumer

An agent can produce three kinds of output:

Human-readable message such as a research summary.
Artifacts such as PDFs, spreadsheets, presentations, or generated files.
Structured data for another program.

Do not force one representation to serve all three consumers.

Human-readable results

Render the final message in the original conversation and preserve source links. Channel adapters may need to split long text, convert Markdown, or post a short summary plus an artifact link.

Artifacts

Treat generated download URLs as temporary external references. Copy required artifacts into storage controlled by your application, scan them, record content type and checksum, and enforce the user’s authorization when serving them later.

The Manus file-upload documentation currently says uploaded files are automatically deleted after 48 hours. Provider retention rules can change, so the application should not rely on provider storage as its permanent archive.

Structured results

For automation, request structured_output_schema when creating the task. Manus runs the agent normally and then performs a post-processing extraction when the task finishes.

That has two consequences:

the JSON contract makes result consumption easier
it does not constrain the agent’s browsing, tool selection, or intermediate behavior

The current schema subset requires an object root, all properties in required, and additionalProperties: false. Validate the result again in your application. A schema-valid value can still be factually wrong.

{
  "type": "object",
  "properties": {
    "recommendation": { "type": "string" },
    "confidence": { "type": "number" },
    "source_urls": {
      "type": "array",
      "items": { "type": "string" }
    }
  },
  "required": ["recommendation", "confidence", "source_urls"],
  "additionalProperties": false
}

Use deterministic application code for calculations, policy decisions, money movement, and other operations that should not depend on a generated interpretation.

Slack: Built-In Integration Or Custom Bridge?

Manus has an official Slack integration. Before building a custom bridge, decide whether your application needs behavior beyond the built-in experience.

Use the built-in integration when:

users only need to invoke Manus and receive normal results in Slack
Manus’s standard thread behavior and permissions are acceptable
there is no product-specific database or workflow to update

Build a custom bridge when:

task creation depends on your own authorization or business rules
requests need preprocessing, templates, or tenant-specific context
results must update internal records as well as Slack
approval must go through your product’s policy engine
you need custom observability, retention, billing, or audit behavior
one task must coordinate across several channels or systems

A custom Slack flow should be event-driven:

sequenceDiagram
    participant U as Slack user
    participant A as Application
    participant D as Job database
    participant M as Manus API
    U->>A: Mention bot with research goal
    A->>A: Authenticate and validate policy
    A->>M: task.create
    M-->>A: task_id
    A->>D: Store Slack thread and task mapping
    A-->>U: Research task accepted
    M->>A: Signed task_stopped webhook
    A->>A: Verify and deduplicate event
    A->>D: Load original Slack thread
    A-->>U: Post question or final result in thread

Slack may retry its own events too. Deduplicate incoming Slack event IDs separately from Manus webhook event IDs.

Reliability And Operational Controls

A hosted agent removes some infrastructure work, but it introduces a remote dependency. Plan for partial failure at every boundary.

Failure	Defensive behavior
Task creation times out	Reconcile before creating a duplicate; store provider request IDs when available
Rate limit	Exponential backoff with jitter and bounded retries
Webhook arrives twice	Unique `event_id`; idempotent state transition and delivery
Webhook is missed	Periodic reconciliation with task messages or detail endpoints
Events arrive out of order	Apply transitions by event identity and current state, not arrival assumptions
Task waits indefinitely	Input and approval deadlines with user reminders or expiry
User cancels	Stop provider task and mark local cancellation intent immediately
Result cannot be delivered	Persist result and retry only the delivery adapter
Connector authorization expires	Surface a reauthorization state rather than silently removing context
Generated file expires	Copy required artifacts into controlled storage promptly
Provider behavior changes	Contract tests, pinned API version, and a dated compatibility review

Current Manus rate limits are per user and shared across that user’s API keys. The exact numbers are version-sensitive. Read response errors, follow the current rate-limit documentation, and avoid treating today’s limits as permanent constants in product documentation.

Track at least:

task creation success and latency
time in running and waiting states
clarification and approval frequency
completion, error, cancellation, and expiry rates
webhook verification and duplicate counts
credits or cost per accepted result
connector and browser usage
artifact download and delivery failures
user-rated or evaluator-rated result quality

Operational success is not the same as research quality. A task can complete cleanly and still return weak, stale, or unsupported conclusions.

Current Manus Pricing

Manus uses subscription plans and credits rather than publishing one fixed price per task. This is important: a credit is an internal usage unit, not a model token.

The following public prices were current on July 22, 2026:

Plan	Starting monthly price	Included usage and access
Free	$0	Chat mode, Manus 1.6 Lite in Agent mode, 300 daily-refresh credits, 1 concurrent task, and 2 scheduled tasks
Pro, 4,000 credits	$20	Manus 1.6 Max, 1.6, and 1.6 Lite; Advanced Research, Wide Research, websites, and slides; up to 20 concurrent and 20 scheduled tasks
Pro, 8,000 credits	$40	The same main Pro capabilities with 8,000 monthly credits; the public offer includes a 7-day trial
Team	$20 per seat	Pro capabilities plus team administration, SSO, usage analytics, internal access controls, shared templates, and a data-training opt-out

Annual billing advertises a 17% discount. These are starting prices; taxes, regional pricing, negotiated plans, and later product changes can alter the actual amount. The live Manus pricing page is the final source of truth.

On the Free plan, daily-refresh credits can only be used with Manus 1.6 Lite, and their documented monthly consumption limit is 1,500 credits.

Credit consumption depends mainly on:

LLM tokens used for planning, decisions, and output generation
virtual-machine work such as browsing, file operations, and code execution
third-party data APIs used during the task
task complexity, duration, retries, and follow-up work

Credits are deducted only while the agent is actively processing. Monthly subscription credits reset each billing cycle rather than rolling over. Purchased add-on credits can carry forward while the paid subscription remains active. Manus says provider-side technical failures receive a credit refund.

API-created tasks expose credit_usage through task metadata, so an application can record actual consumption per task. However, Manus does not publish a stable conversion such as “one credit equals a fixed number of tokens” or a guaranteed dollar price for a research task. Use the dashboard estimate before a manual task, store actual credit_usage for API tasks, and calculate the distribution of cost per accepted result from production data.

Manus Versus Direct OpenAI Or Anthropic API Calls

The comparison is not one agent call versus one model call. A Manus task may make many model calls, browse several sites, run code, use a virtual machine, query paid data sources, pause for input, and create files. A direct OpenAI or Anthropic API request usually buys model inference; even when hosted tools are available, your application chooses the tools and owns more of the surrounding loop.

Dimension	Manus hosted agent	Direct OpenAI or Anthropic API
What you buy	A managed task that can plan, use tools, and deliver artifacts	Model input and output, with optional separately priced tools or managed features
Billing unit	Variable Manus credits covering tokens, compute, and some third-party services	Published price per million input and output tokens, plus applicable tool charges
Orchestration	Manus runs the inner agent loop	Your code or framework defines the loop, tools, limits, and stopping rules
Infrastructure	Hosted browser, sandbox, files, connectors, and task state are part of the service	You provide or configure search, connectors, sandboxing, queues, files, and durable state
Cost predictability	Lower before the first run because task complexity changes credit use	Higher at the model-call level because tokens can be measured and budgeted
Control	Faster to adopt, but provider behavior and credit accounting are more abstract	More implementation work, but finer control over models, prompts, tools, retries, data, and spend
Best fit	Open-ended work where managed execution removes substantial engineering	Bounded features or high-volume workflows where control and unit economics matter

For a concrete price reference, these direct API rates were current on July 22, 2026. Prices are USD per one million tokens and exclude tool, storage, search, sandbox, data-provider, and application-infrastructure charges.

Provider and model	Input	Cached input	Output
OpenAI GPT-5.6 Luna	$1.00	$0.10	$6.00
OpenAI GPT-5.6 Terra	$2.50	$0.25	$15.00
OpenAI GPT-5.6 Sol	$5.00	$0.50	$30.00
Anthropic Claude Haiku 4.5	$1.00	$0.10 cache hit	$5.00
Anthropic Claude Sonnet 5	$2.00 through August 31, 2026	$0.20 cache hit	$10.00 through August 31, 2026
Anthropic Claude Opus 4.8	$5.00	$0.50 cache hit	$25.00
Anthropic Claude Fable 5	$10.00	$1.00 cache hit	$50.00

Claude Sonnet 5 moves to standard pricing of $3 input and $15 output per million tokens on September 1, 2026. Anthropic’s cache-hit column is not identical to OpenAI’s cached-input mechanism, so compare each provider’s complete caching rules before estimating savings.

For example, an uncached call with 100,000 input tokens and 10,000 output tokens costs about $0.40 on GPT-5.6 Terra and $0.30 on Claude Sonnet 5 at its current introductory rate, before tools and infrastructure. The same arithmetic cannot honestly price a Manus task because the number of internal model calls, tool actions, VM work, and third-party requests varies by task.

The fair comparison is total cost per accepted outcome:

provider usage
+ search, data, and tool charges
+ sandbox, storage, queue, and observability costs
+ engineering and operations
+ retries, failures, and human review
= total cost per accepted result

Manus can be cheaper overall when it replaces a substantial amount of orchestration and operations work. A direct model API can be cheaper and easier to forecast when the workflow is narrow, repeated at high volume, or already has the required tools and infrastructure.

Why Use Manus?

Manus is a reasonable fit when the product needs a managed general agent rather than only text generation.

It is strongest when:

tasks require web research and synthesis across many steps
users expect reports, files, presentations, or other artifacts
the agent needs a hosted sandbox and tool ecosystem
connectors and browser-based work are central to the use case
the team wants to avoid building and operating a complete agent runtime
occasional human clarification fits the product experience
vendor-hosted execution and data processing meet policy requirements

The central benefit is speed of integration. Your application delegates the research execution loop while retaining its own user experience and business controls.

Why Not Always Use Manus?

A hosted general agent is not the right default for every feature.

Choose a direct model API when:

one bounded prompt produces the answer
latency must be measured in seconds
your code already owns the required retrieval and tools
every tool call and token needs tight control
a simpler failure model is more important than agent autonomy

Choose a self-hosted agent framework when:

you need to inspect or customize every loop decision
models, tools, memory, and execution environments must be replaceable
data cannot be processed by the hosted provider
domain-specific orchestration matters more than general autonomy
your team is prepared to operate sandboxes, queues, state, and observability

Add a durable workflow system such as Temporal when:

the application’s multi-service process must survive deployments and crashes
work spans Manus plus databases, payments, approvals, or other agents
retries, timers, compensation, and long waits need explicit semantics
the provider task is only one step in a larger business workflow

Manus and Temporal solve different layers. Manus can perform the research task; Temporal can durably coordinate when to start it, wait for it, route approvals, combine it with other services, and recover the surrounding workflow. See Durable AI Agents with Temporal.

Use ordinary deterministic services when:

the workflow is a known sequence of API calls
correctness depends on explicit business rules
there is no meaningful judgment or open-ended research

An agent should earn its place by handling uncertainty. It should not replace code that is already easy to specify and test.

Prototype The Task Before Building The Integration

Test the research behavior in the Manus product before wrapping it in Slack, webhooks, queues, and databases.

Use this sequence:

Run representative tasks manually in Manus.
Identify what context produces good results.
Record common clarification questions and failure modes.
Define the expected human output and machine-readable contract.
Decide which connectors and actions are actually required.
Build an evaluation set from real examples.
Only then automate task creation and delivery.

This separates two problems:

Can the agent perform the task well?
Can the application operate the task reliably and securely?

If the first answer is no, more integration code will not fix it.

Implementation Checklist

Before development:

Confirm that a hosted agent is preferable to a direct model call or deterministic service.
Test representative tasks manually and define quality criteria.
Review current API version, profiles, limits, and retention rules.
Decide whether the built-in integration already satisfies the use case.
Complete vendor, privacy, residency, and procurement review.

Before task creation:

Authenticate the local user and tenant.
Validate goal, files, connectors, browser access, and expected output.
Keep credentials and routing metadata out of prompts.
Default task sharing to private.
Create a local job record and define deadlines and cancellation behavior.

Before production: