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What the four companies meant by agentic AI
The discussion took place during VentureBeat’s AI Impact Tour event, “Agentic AI — the next giant leap forward in the AI revolution,” presented by Outshift by Cisco. VentureBeat published its recap on October 1, 2024. The panel brought together Mano Paluri of Meta, Vijoy Pandey of Outshift, Paige Costello of Asana and Kumar Sricharan of Intuit, with VentureBeat CEO Matt Marshall moderating. VentureBeat’s event recap describes a discussion, not a four-company partnership announcement.
Operationally, an agentic AI system pursues a goal across multiple steps. It may retrieve information, choose a tool, make a plan, take an action and reassess what happened. That is different from a chatbot whose main job is to generate a response to one prompt. The label “agentic” does not guarantee that a system is accurate, safe or authorized to make consequential decisions.
Autonomy is better understood as a range: an assistant can suggest an answer; a simple automation can perform one defined action; a bounded agent can coordinate a sequence of steps; and a higher-autonomy system can act with less frequent intervention. The 2024 speakers mainly discussed the middle of that range, where software operates within a defined product or business process.
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Four perspectives on the agentic stack
| Company | Emphasis at the event | Example discussed |
|---|---|---|
| Meta | Personalized, role-specific agent systems | Different assistants for users, businesses, creators, billing or advertising tasks |
| Outshift by Cisco | Infrastructure, coordination and interoperability | Predictive IT diagnostics and remediation |
| Intuit | Domain-specific financial automation and decision support | Small-business onboarding and analysis of tax-code changes |
| Asana | Workflow-aware autonomy and coordination | Prioritizing requests, checking for missing information and routing work |
The shared premise is that useful agents need more than a capable model: they need relevant context, access to tools and data, a workflow to operate within, and rules about what they can do. The four companies emphasized different layers of that system rather than proposing one common architecture. The event account provides the basis for these examples.
Meta: a family of agents, not just one assistant
Meta’s Mano Paluri argued that companies should begin working on agents even though the technology had not matured enough to fulfill its full potential. Meta’s framing was a shift from treating AI as one model to building systems from customizable components. In that vision, different agents could serve different roles or users, from personal assistance to billing, creator and advertising work.
That was a forward-looking description at the 2024 event, not a claim that Meta’s then-current consumer assistant could autonomously perform all those jobs. Later developments offer a separate point of comparison. In June 2026, Meta announced Meta Business Agent and a Business Agent Platform for business-customer interactions. Meta said the agent could answer business-specific questions, recommend products, book appointments, qualify leads, escalate to staff and close sales. Meta also said more than one million businesses were already using a Meta Business Agent on WhatsApp and Messenger at the time of the announcement, and that expansion to Instagram and globally was under way. Meta described getting started as free at launch, with paid subscription offerings planned for later. Those are Meta’s own claims and launch terms, not evidence that the wider agent ecosystem envisioned in 2024 is complete. Meta’s June 2026 announcement sets out the product description.
Asana: autonomy inside a workflow
Asana’s example made the boundary of autonomy easier to see. In chat and workflow contexts, an agent could receive a request, assess its priority, check whether it had enough information and identify who should be involved. Creative requests, revisions, feedback and approvals were examples of work that can involve repeated coordination.
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The central question is not only whether AI can complete a task; it is how much decision-making authority to grant it. A workflow can give an agent context and a place to route work, while people retain responsibility for ambiguous or consequential decisions. That can reduce coordination overhead without pretending to automate an entire job. The event recap does not establish that these capabilities were available to every Asana customer or plan. VentureBeat’s account describes the examples discussed.
Intuit: financial work benefits from context, but raises the stakes
Intuit described agents for small-business onboarding that could gather and use information from multiple sources. It also discussed experimentation across its financial products in areas where hand-built rules could be difficult or costly to maintain. Internally, agents could help track changes to tax codes, connect those changes to affected software code and suggest changes for developers. That is decision support and software maintenance, not a claim that an agent independently interprets tax law or guarantees compliance.
Financial workflows warrant tighter controls than low-impact productivity tasks. A mistaken summary may be inconvenient; an incorrect change to records, payroll or a tax-related process can have broader consequences. In practice, authorization, audit trails, privacy safeguards, explainability and human review matter alongside the model’s ability to complete steps.
By 2026, Intuit was presenting a broader commercial version of this direction. Its Intuit Assist positioning spans products including TurboTax, Credit Karma, QuickBooks and Mailchimp. Its Enterprise Suite AI agents page promotes AI-supported reconciliation, financial summaries, payroll workflows and project-management automation. Intuit says its AI-powered reconciliation feature is based on internal data comparing opted-in and non-AI users as of November 2025. It also says project-management AI reduced average setup work by 69% in an internal user-data comparison as of September 2025. These are vendor-reported comparisons, not independent benchmarks, and the page’s statements should not be generalized beyond their stated basis.
Outshift: the infrastructure problem between agents
Outshift’s contribution was the systems-level view: a possible distributed “internet of agents,” in which independently built systems can find one another and cooperate. Its proposed stack involved open models and tools, orchestration, discovery, secure communication, state exchange and ways to manage probabilistic results. Outshift also described a multi-agent predictive diagnostics and remediation tool for enterprise technology stacks, aimed at predicting IT problems, finding likely causes and recommending or applying mitigations.
Three problems stood out in the event discussion:
- Discovery: How does one agent find another and understand its capabilities?
- Collaboration under uncertainty: How can agents coordinate when an intermediate result may be incomplete or wrong?
- Communication: How can agents work together when natural language is flexible and ambiguous, unlike a rigid API contract?
Calling for open standards is not the same as having a universally adopted standard. Organizations may value interoperability across vendors, but they may also prefer a tightly integrated proprietary environment for support, security or accountability. The event presented the open-agent-network idea as a direction, not a settled industry architecture. Outshift’s position is summarized in the event recap.
What a practical agent system needs
The panel’s varied use cases can be translated into a practical architecture. The model is only one component; each layer shapes what the agent can know and do.
- Foundation model: Interprets instructions and produces reasoning or language.
- Enterprise context: Retrieves the relevant records, policies or documents, subject to access rules.
- Tools and APIs: Provide defined ways to search, create, update or execute work in other systems.
- Planner or orchestrator: Breaks a goal into steps, chooses tools and handles handoffs.
- State and memory: Keep the right task context across multiple steps without confusing users, cases or time periods.
- Policy and permissions: Limit the agent’s read, write and execute authority to what its task requires.
- Human approval: Routes uncertain or consequential actions to an accountable person.
- Logs, evaluation and recovery: Record actions, measure complete task outcomes and provide retry, escalation or rollback paths.
This is a practical synthesis of the issues raised in the discussion, not an architecture that the four companies jointly endorsed. It helps explain why improving a model alone does not solve integration, authorization or operational reliability.
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Multi-step systems introduce failure modes beyond an incorrect answer. An agent can misunderstand a goal yet execute its mistaken interpretation consistently. Access to information can be mistaken for permission to act. In a multi-agent chain, one system’s unsupported assumption may become another’s input. Free-form conversational handoffs can also make it hard to reconstruct why a decision was made.
- Ambiguous requests: Ask for clarification or confirmation before acting when different interpretations could lead to materially different outcomes.
- Excessive permissions: Separate read, recommend and execute privileges; grant only what the workflow needs.
- Cascading errors: Preserve the source of facts and validate important handoffs rather than treating another agent’s output as ground truth.
- Weak observability: Log prompts, tool calls, decisions, changes and escalations so an operator can investigate an outcome.
- Unclear recovery: Define how to retry, reverse an action or hand the task to a person.
- Unmeasured costs: Include model calls, tool use and human review when assessing whether automation is worthwhile.
- Unclear accountability: Keep a named human or organization responsible for consequential outcomes.
These are operational implications of deploying agents; they should not be confused with a claim that the speakers reported specific failures. The event directly highlighted discovery, collaboration under uncertainty and communication as unresolved technical challenges.
How to choose a first workflow
For a business owner or technology team, the safest starting point is a workflow that is useful but contained: repetitive, governed by clear rules, rich in accessible context, measurable and reversible. Request triage, internal knowledge retrieval, document intake, project follow-up, reconciliation suggestions or developer assistance with mandatory review are more suitable first candidates than irreversible financial transfers or unsupervised production changes.
Before a pilot, write down the agent’s authority and the conditions for human intervention:
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- What information may it read, and what systems may it change?
- Which actions can it take automatically, and which require approval?
- What uncertainty, missing data or policy conflict triggers escalation?
- How will success be measured across the complete task, not just the quality of one response?
- Can changes be reversed, and can an operator inspect the full action history?
- Does the workflow justify an embedded product, a custom build or a broader enterprise platform?
An embedded agent may be the quickest fit if the process already lives in accounting, customer-service or project-management software. A custom model-platform build offers more control but leaves the organization responsible for orchestration, evaluation, security and upkeep. A multi-system enterprise platform may support broader workflows, with added implementation effort and potential vendor dependence. The right choice depends on the organization’s existing systems and control requirements, not on which product uses the most expansive “agent” label.
What changed after the 2024 discussion
By 2026, Meta and Intuit were publicly describing commercial products that put some of the event’s ideas into customer-facing or business workflows. Meta’s Business Agent is an example of a role-specific agent for customer interactions; Intuit’s current product pages position AI across financial and operational tasks. These developments show that parts of the vision have moved into product offerings. They do not demonstrate that agents from different vendors can yet form the open, interoperable network Outshift imagined, or that high-autonomy operation is appropriate for every business process.
The durable lesson from the event is that agentic AI is less about giving a chatbot more initiative than designing software systems in which AI can access the right context, use constrained tools and operate under clear accountability. For organizations considering it, the essential decision is not whether to buy into the word “agentic,” but which bounded task is worth delegating—and what safeguards must surround that delegation.
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