Agentic AI describes systems in which an AI agent can choose and carry out steps toward a goal, using tools and feedback within limits set by people. In a system built around a language model, the agent interprets the objective, selects an action, examines the result and decides what to do next. It can stop, ask for help or continue while its permissions and operating limits allow.
For a company, the useful question is where that ability belongs in the work. An agent might investigate a late delivery, compare the available evidence and prepare a recommendation. Someone still needs to define the acceptable outcome, decide what information it may access and control whether it can change an order.
This guide explains how that works, how RAG and memory contribute, and how people and agents can organize ongoing work in Swarm.
What makes an AI system agentic?
The defining characteristic is some control over the steps taken toward an objective. An agent can use an observation to choose a different next action: read another source, run a check, revise a plan or ask a person a question. The available choices depend on the application and the authority it has been given.
Google Cloud's overview of agentic AI describes a cycle of gathering information, reasoning, planning and acting. In practice, an application also needs a way to establish whether the work should continue or stop.
A useful starting specification covers five things:
The outcome the agent is working toward
The information and tools it can use
The actions it is allowed to take
The evidence required to accept the result
The conditions that require a pause or human decision
The term does not imply consciousness, general intelligence or unrestricted autonomy. A system may be agentic within a narrow responsibility, such as investigating a failed test, while being unable to deploy a fix or access another project.
How does an AI agent work in practice?
An agent usually operates through a loop connecting a model, application code and tools. AWS's explanation of AI agents describes identifying a goal, acquiring information and carrying out tasks while examining progress.
Consider this brief for a manufacturing team's assistant:
Investigate the delayed control units for the assembly line. Use the supplied delivery notices, accepted specifications and approved supplier list. Prepare a sourced comparison of the available options. Do not contact suppliers, place orders or change the production schedule. Ask the operations lead to resolve missing requirements.
The agent can begin with the latest delivery notice. If it finds a revised delivery date, it can compare that date with the required installation window. If a possible replacement has a different specification, it can inspect the relevant technical document before recommending it.
Each tool result changes what the agent knows. A missing document should produce an unresolved question. A failed lookup should remain a failed lookup. The application can limit the number of actions, elapsed time or spending allowed before requiring another decision.
The important distinction is between the requested action and its observed outcome. A tool call asking a system to save a record is an attempt. A successful response and an appropriate check provide evidence that the record was saved. For consequential actions, the application needs to handle uncertain responses and repeated requests carefully so a retry cannot silently duplicate the action.
Completion also needs a definition. For this investigation, an acceptable result could be a comparison covering delivery, technical compatibility, supporting sources and unresolved questions. The agent does not need authority to buy anything to produce useful work.
How are agentic AI, AI agents and workflows related?
An AI agent is a component that can choose actions. Agentic AI describes the broader approach or system built around that behavior. A system can use one agent or several; the number of agents alone does not establish how capable or autonomous it is.
A workflow specifies how work proceeds. It may follow fixed steps, include an agent within one step, or let an agent choose part of the sequence. In Building effective agents, Anthropic distinguishes predefined workflow paths from systems in which the model directs its process and tool use.
These approaches can coexist. A company might always require a technical review before a purchasing decision while allowing an agent to choose how to gather the evidence for that review.
Generative AI describes the production of content, such as text or code. An agent can use that capability to write a comparison or propose a patch. Whether the surrounding system can act, preserve progress and enforce approval depends on its tools and application design.
The interface is a separate question. A chat window can provide access to an agent, a document search tool or a fixed workflow. The appearance of the conversation does not tell you which actions the system can perform.
How do AI agents use RAG and memory?
Retrieval augmented generation, or RAG, brings relevant external information into a model's context when it produces an answer. An agent can use retrieval to find a specification, check a prior decision or locate the source behind a recommendation. Our practical guide to RAG and AI memory explains that process in more detail.
For ongoing work, three layers are worth keeping distinct:
Current context is the information available to the model for its present decision.
A saved project record retains sources, outputs, decisions and progress beyond the current session.
Retrieval selects information from available records for the next question or action.
An agent investigating the control units might need the accepted voltage requirement and the delivery deadline. It may have little use for an unrelated discussion about the factory's office equipment. Access to a large record does not mean every part belongs in every model call.
Anthropic's guidance on context engineering treats context as a limited resource that needs deliberate selection and maintenance. Saving a useful checkpoint helps a later session identify the next step without replaying an entire conversation.
A checkpoint for this investigation could record the current objective, documents checked, alternatives ruled out, remaining uncertainty and the person responsible for the next decision. A new participant can then inspect the underlying evidence before continuing.
Retaining a correction in project memory also differs from training a model. The correction can inform later authorized work when it is retrieved. It does not, by itself, change the model's weights or guarantee that every future answer will apply it correctly.
When should multiple AI agents work together?
Multiple agents can be useful when a task contains genuinely distinct responsibilities. In the supplier investigation, a technical specialist could examine compatibility while another participant compares delivery evidence. A person can review the combined recommendation.
The handoff needs enough structure to make those contributions useful. Each participant should know its assignment, available sources, expected output and the decisions it cannot make. Results should identify which evidence supports a conclusion and which questions remain open.
Shared records do not automatically prevent duplicate work or conflicting conclusions. Two agents reading the same outdated source can make the same mistake. Giving both permission to change the same external record can create a conflict even when each action seems reasonable in isolation.
Assign responsibility for each change and establish how conflicting proposals will be resolved. A clear division could allow one agent to prepare a technical assessment and another to prepare a delivery comparison, while reserving acceptance of the combined recommendation for the operations lead.
Start with one agent when it can do the work reliably. Add another when the separation improves access control, specialist analysis or independent checking enough to justify the additional coordination and cost.
A worked example: investigating a supplier disruption in Swarm
The following is an illustrative workflow, not a customer case study or a report of an executed Run. It shows how a team could organize the investigation using Swarm's documented capabilities. Access to a supplier system, email account or purchasing tool would need its own supported integration and authorization.
Establish the shared brief
The operations lead creates a Space for the assembly line project and saves the accepted requirements, delivery notice and relevant supplier evidence. The objective and restrictions belong in the brief so a later participant can understand them too.
The Space becomes the shared project record. The team's purchasing system can remain the authoritative place for orders, with relevant evidence deliberately saved or connected to Swarm. A Space does not automatically import private chats or every company document.
Connect an agent with the necessary access
The team connects a compatible agent and grants access to the relevant Space. Swarm supports connections through MCP and the API, as described in its connection guide. The grant determines which records and operations are available to that connection.
The agent can inspect the brief and permitted evidence, then identify missing information before it begins the comparison. A tool being available in a client does not grant access to every Space or external system.
Record the work and inspect the attempt
In Swarm, a Task is a saved brief and a Run is an execution attempt. The team can keep the requested investigation separate from the attempts made to complete it. Outputs and available execution records help a reviewer inspect what happened.
The agent needs an active execution host to do the work. Saving a Task or connecting a client does not keep a closed conversation running. Configured Swarm Cloud execution is a separate route with its own permissions and billing controls.
Save a comparison with its evidence
The agent saves an Artifact containing the candidate options, cited sources and unresolved questions. It also records a checkpoint that allows the next authorized participant to resume the investigation.
If the evidence establishes delivery timing but leaves technical compatibility uncertain, the comparison should say so. The reviewer can ask for another investigation or provide the missing requirement before accepting a recommendation.
Review the result before acting
The operations lead examines the comparison and records the accepted decision. Swarm distinguishes approval to execute work from review of its result. Permission to run an investigation does not mean its recommendation has been accepted.
Changing an order is a further action. The purchasing integration and its execution controls must enforce the relevant authority. A sentence in a prompt telling the agent to ask first is not sufficient protection for a tool that can otherwise act without restriction.
Carry the decision into the next stage
An authorized engineering participant can later retrieve the accepted requirements, supplier comparison and review record. A corrected specification or changed delivery date should be recorded with enough evidence to explain what it replaces.
This is where shared memory becomes useful beyond one person's chat. Procurement, engineering and operations can contribute at different times while keeping the relationship between the evidence, proposed work and accepted decision inspectable.
What else can companies use agentic AI for?
The same operating pattern applies to other responsibilities. These examples describe possible workflows that require appropriate data access, integrations and review.
Software maintenance across teams
An agent investigates a reported regression using permitted issue records, test results and code. It can prepare a patch and save the tests it ran. A maintainer reviews the evidence before merging, while the accepted finding remains available to a later investigation.
Customer commitments during delivery
An implementation team records the agreed scope, outstanding dependencies and accepted changes for a customer project. An agent can compare a proposed update with that record and prepare a review brief. The responsible person decides whether the company should make a new commitment.
Research that continues across experiments
Researchers retain hypotheses, methods, outputs and review notes in a shared record. Agents can compare supplied findings or prepare the next experiment brief. Researchers remain responsible for checking methods and distinguishing an interpretation from a reproducible result.
Recurring operational reviews
A team can define a recurring review of supplied maintenance or quality reports. An agent prepares a summary of changes, identifies missing evidence and proposes follow up work. A schedule still needs an available executor, appropriate permissions and a clear escalation path when the work cannot proceed.
How should teams control and evaluate agentic work?
Decide what a failure would mean before expanding an agent's authority. An incomplete internal comparison has different consequences from an incorrect order or a published customer commitment.
Separate the ability to read evidence, save a proposal, change an external system and accept a result. Give each participant the access its responsibility requires. Treat retrieved documents and messages as evidence to examine; their contents cannot grant new permissions.
Start with a small set of realistic test cases. Include an ordinary success, a missing source, contradictory evidence, an unavailable tool and a situation that must stop for human approval. Introduce an outdated decision and check whether the agent notices the replacement.
Evaluate the observable result:
Did the output satisfy the brief and cite the evidence it used?
Did the agent identify uncertainty and missing access?
Did required approval occur before a consequential action?
Could a reviewer distinguish an attempted action from a confirmed outcome?
Could another authorized session resume from the saved record?
Were the time, token use, tool costs and review effort acceptable?
An agent's own account of success is one input. Check the saved output, relevant tool results and actual state of any system it changed. Keep the authority narrow until those checks show that the workflow behaves as intended.
Common questions about agentic AI
Does agentic AI need several agents?
No. A single agent can choose actions and adapt to feedback. Several agents may help divide distinct responsibilities, but they also introduce handoffs, additional cost and opportunities for disagreement.
Can agentic AI work with human approval?
Yes. An agent can investigate, prepare a proposal and pause for an authorized decision. The application and connected tools need to enforce the boundary, including what can happen before approval and how work resumes afterward.
Is RAG enough to make an AI system agentic?
RAG supplies retrieved information for generation. A system becomes agentic when it also gives an agent some control over choosing and carrying out steps. A document question answering tool can use RAG without managing an ongoing task.
Does an agent learn automatically from every task?
Learning can mean different things. A system may save feedback, retrieve an accepted correction or revise its current plan. Updating model weights requires a separate training process. Check which behavior a product actually supports before assuming that saved activity improves future performance.
When is a simpler workflow a better choice?
Use a predictable workflow when the required steps are known and the exceptions are manageable. A fixed sequence can be easier to test and supervise. Anthropic's guidance on building agents recommends starting with the simplest approach that meets the need and adding complexity when it is justified.
Start with one reviewable responsibility
Choose a responsibility with a clear outcome, identifiable sources and a person who can review the result. Define what the agent can read, what it can save and where it must stop. Try the workflow on a small case and inspect the evidence before granting broader access.
In Swarm, that can begin with a Space containing the accepted brief and relevant records, a connected agent, and a Task whose result someone can meaningfully evaluate. The first goal is a useful piece of work that the next person or agent can understand and continue.
Explore Swarm Spaces or read the agent connection guide to see the current capabilities and operating boundaries.