An AI agent is one goal-directed system using a model, context, and permitted tools to pursue a bounded task. Agentic automation is a broader operating design that coordinates decisions across multiple steps, tools, agents, or responsibilities. Choose an agent when context must determine the next permitted action. Choose broader orchestration only when separate responsibilities, permissions, or processes must work together.
This is a useful design model based on Make’s terminology, not a universal industry taxonomy. Other frameworks emphasize agents, tools, handoffs, guardrails, or orchestration without using the same categories. An LLM inside a fixed workflow does not automatically make the whole workflow agentic.
The practical question is not which label sounds more advanced. It is what the system may decide, what it may access, which tools it may call, and which actions require deterministic validation or human review.
The short answer: one bounded agent versus coordinated automation
Make describes an AI agent as a goal-oriented combination of a large language model, context, and tools. Its broader description of agentic automation focuses on coordination across agents, workflows, and processes toward a larger outcome. Make’s explanation of AI agents and agentic automation supplies that framing, while the OpenAI Agents SDK documentation shows a different vocabulary built around agents, tools, handoffs, guardrails, and tracing.
Use the least autonomous design that handles the real uncertainty:
- Known action: use deterministic automation.
- Known workflow, unclear input: add an AI step for extraction, classification, or summarization, then return to fixed rules.
- Context-dependent next action: use one bounded agent with an allowlist of tools.
- Distinct responsibilities or permissions: coordinate agents or processes only when that separation solves a demonstrated operating constraint.
Separate the labels from the operating decision
Autonomy is not an on or off switch. A workflow can be partly deterministic and partly agentic. The useful boundary is the point where the system interprets information or selects among approved actions.
| Approach | Best fit | Example decision |
|---|---|---|
| Deterministic automation | Structured input and stable rules | Route an invoice using a supplier ID and fixed amount bands. |
| AI-assisted automation | Fixed process with one interpretation step | Extract fields from an email, then apply fixed routing rules. |
| Bounded AI agent | Context determines a permitted next action | Choose between an approved search tool and a narrowly scoped scenario. |
| Coordinated automation | Separate roles, tools, or permissions must cooperate | Delegate classification, then pass the result to a process owner. |
Use it when the action is known
Keep routing, permissions, and writes in explicit rules. Let the model interpret an unstructured input without granting it unrestricted control of the destination.
Use it when tool choice is uncertain
Give the agent a defined goal and limited tools. Add coordination only when ownership, access boundaries, or distinct expertise justify the extra handoffs.
Make recommends deterministic automation for predictable, structured work and agentic approaches for ambiguous inputs or changing rules. That is Make’s product guidance, not independent comparative-performance research. Its guidance on when to use AI agents also supports hybrid designs that combine rules, AI-powered steps, and agentic decisions.
Choose the smallest level of autonomy that solves the problem
- Test process stability. If the process is stable and the input is structured, start with rules. Do not add a model merely because one is available.
- Locate the ambiguity. If only one step needs extraction, classification, or summarization, add AI at that step and keep the next action fixed.
- Test the next action. If context determines which of several approved tools should be used, consider a bounded agent. Define the tool allowlist, data access, and permitted outcomes first.
- Test the reason for separation. Use multiple agents only when separate permissions, expertise, or ownership materially improve control or execution. More agents add handoffs and failure points.
If the action is known, keep it deterministic. If the action must be selected from context, bound the choice before increasing autonomy.
A practical progression is to establish a deterministic baseline, add AI where interpretation is needed, then permit bounded tool choice only if the next action cannot be fixed in advance. Make’s current AI Agent (New) documentation describes one level of sub-agent nesting for that workflow: a parent can use a sub-agent, but that sub-agent cannot call another sub-agent. This is a Make-specific product constraint, not a general limit for other platforms.
If you are assessing a bounded use case, AI agent consulting can help define the task, tools, and controls before implementation.
Three implementation patterns, from an AI step to delegated work
The patterns below are hypothetical operating designs. Make documents the relevant agent, scenario, input, output, and response-format mechanics, but the field contracts, business rules, destination behavior, and exception ownership must be designed for the actual process.
| Trigger and source | AI job | Validation and action | Fallback owner |
|---|---|---|---|
| Inbound email or webhook with a source message ID | Classify intent and urgency; return structured fields. | Check IDs, allowlisted values, customer match, confidence policy, and duplicate status before a verified CRM update. | Operations or CRM owner handles missing identity, unsupported intent, and duplicate conflicts. |
| Validated request supplied to Make AI Agent (New) | Choose whether to call one permitted scenario tool. | An on-demand scenario validates inputs and authorization, performs the approved action, and returns a stable status and record ID. | Workflow owner handles rejected or failed results. |
| Task with a distinct research or classification responsibility | Parent delegates a bounded subtask to a sub-agent. | Validate the returned status and result, then route to a deterministic next step or review. Assign one owner for each business write. | Parent workflow owner handles invalid delegation or unresolved decisions. |
Pattern 1: classify an inbound email before a CRM write
Suppose a support email asks to update a contact detail. The source system supplies a message ID and customer ID. The AI step proposes an intent and urgency. It does not choose arbitrary CRM fields, grant itself permissions, or write directly without a deterministic gate.
{
"source_message_id": "msg_illustrative_1042",
"intent": "profile_update",
"urgency": "normal",
"customer_id": "cust_illustrative_208",
"confidence": 0.91,
"needs_review": false,
"prompt_version": "intake-v1"
}These fields are illustrative, not a Make-published schema. Before mapping them downstream, define required fields, accepted values, numeric ranges, null handling, source provenance, and explicit success, rejected, and failed statuses.
Pattern 2: let an agent call a controlled scenario
Make documents scenarios acting as agent tools through explicit inputs and outputs. The called scenario should be configured on demand and use a Return outputs module when its result must return to the agent. Put identity checks, allowed-action rules, required-field validation, and destination-specific logic inside that deterministic scenario.
A useful illustrative output is:
{
"request_id": "req_illustrative_501",
"status": "updated",
"destination_record_id": "record_illustrative_72",
"error_code": null
}Return explicit statuses such as updated, rejected, and failed. The scenario owner, rather than free-form agent reasoning, decides whether the requested action is authorized and whether the destination write is valid. Confirm the exact app, module, writable fields, authentication scopes, limits, and plan before describing the connection as deployable.
Pattern 3: delegate a distinct responsibility
A parent agent can delegate a narrowly scoped classification or research task to a sub-agent when the responsibility has a distinct tool set or ownership boundary. Return stable task and source-record identifiers with the result. The parent or a deterministic workflow should decide what happens next.
Do not let separate agents independently write the same business object unless one component owns the write and the destination enforces uniqueness or an equivalent concurrency control. For Make-specific scenario and destination design, Make automation consulting is a relevant service.
Keep interpretation separate from enforcement
A structured response makes fields easier to map. It does not prove that values are correct, authorize a write, or show that a business rule passed. Make’s AI Agent (New) documentation describes custom response formats and execution metadata, but those features are not substitutes for business validation or a complete business audit record.
- Define the contract: specify identifiers, enumerated values, numeric types, null policy, provenance, run ID, and success, rejected, and failed statuses.
- Enforce rules deterministically: check required fields, allowed values, identity matches, permission-sensitive actions, numeric bounds, and review thresholds before a write.
- Preserve the source: retain the original record and store the AI proposal and validation result separately before applying a change.
- Restrict access: give tools and credentials only the data and actions needed for the bounded task.
- Escalate consequential ambiguity: route identity conflicts, unsupported requests, failed checks, deletions, billing changes, and external messages to a named person unless an approved policy says otherwise.
In the email example, the model can propose profile_update and normal urgency. Deterministic rules decide whether the customer is unique and whether that action is permitted. For system-of-record and field-mapping decisions, see CRM systems consulting.
Design for retries, duplicates, and the right data grain
Recovery and duplicate prevention are different controls. Make documents retry handling for incomplete executions, but a retry does not make an external write idempotent. If a destination accepts a change before a timeout, a second attempt can repeat the side effect.
A retry is recovery behavior, not duplicate protection. Use a key at the row’s actual grain and enforce uniqueness where the write occurs. A separate check-then-create sequence is not established as race-safe for concurrent runs.
Define what one row represents before choosing a key:
- Event record: one source event. A proposed key is
source_system + source_event_id. - Agent-run observation: one execution. A proposed key is
workflow_id + run_id, so two runs on the same day remain distinct. - Citation or extracted item: one item from one run. Use
run_id + citation_idor another stable item identifier. - Period summary: one defined aggregation across a stated period and dimensions. Keep it separate from event and run rows.
These are proposed design patterns, not Make-published fields. Make Data stores support keys and documented add, replace, update, and insert-missing options. The documentation does not establish universal transactional behavior for concurrent writers. Where concurrency matters, prefer a destination-enforced unique constraint, a verified transactional upsert, or a write that fails safely on a duplicate key.
Retry transient network, rate-limit, or provider failures when the destination action is protected. Route validation, authorization, malformed-output, and duplicate conflicts for correction or distinct handling. If the destination cannot establish whether a write succeeded, send the uncertain result to an owner who can check before resubmission. See Make’s Data store documentation and retry error-handler guidance.
A practical readiness test before scaling
Before expanding from an AI step to an agent, or from one agent to coordinated responsibilities, verify the operating design rather than assuming greater autonomy will improve it. Make currently documents AI Agent (New) as open beta and warns that functionality and pricing may change. Confirm current product naming, provider availability, limits, plan terms, and the exact destination app and module at implementation time.
- A named process owner and system of record are documented.
- Input and output fields, allowed values, null handling, and failure statuses are explicit.
- Tools, credentials, data access, and permitted actions are bounded.
- Deterministic checks run before business writes, with a named exception owner.
- The destination’s uniqueness, upsert, and retry behavior have been verified for the intended action.
- The team can measure valid-output rate, human-review rate, duplicate-action rate, exception-resolution time, and completion of the intended outcome.
Start with rules for stable decisions, add AI for bounded interpretation, and use an agent when context must guide tool choice. Coordinate agents only when separate responsibility or access solves a demonstrated constraint. Measure failure, review, duplicate, and outcome rates before increasing autonomy.