When Your AI Teammate Starts Freelancing: Rethinking Human–Agent Alignment

A workflow looks calm until the AI starts improving it.

At first, this sounds like good news. The system does not merely answer a question. It decomposes a task, chooses tools, drafts intermediate artifacts, revises the plan, anticipates what the human may want next, and quietly reorders priorities along the way. Everyone wanted a teammate. Congratulations. Now the teammate has initiative.

That is where the familiar language of “human-in-the-loop” begins to look slightly underfunded. A loop is tidy. Agentic AI is not. It does not only produce outputs; it generates trajectories. It does not only summarize information; it constructs representations. It does not only follow objectives; it may adapt the working balance among objectives across episodes.

The paper Visioning Human–Agentic AI Teaming: Continuity, Tension, and Future Research by Bowen Lou, Tian Lu, T. S. Raghu, and Yingjie Zhang is useful because it does not treat this as a branding upgrade from “AI assistant” to “AI agent.” It treats agentic AI as a structural change in the conditions of human–AI teaming.¹ Its central move is to use Team Situation Awareness, or Team SA, as a framework for asking a more precise question: not whether humans and AI can agree at one moment, but whether they can remain aligned as action unfolds, representations shift, and objectives evolve.

That distinction matters. Most enterprise AI governance still behaves as if alignment is secured by defining a goal, checking an output, and maybe adding an approval button. This works tolerably well when the system is bounded, the task is discrete, and the AI does not keep revising the road while driving on it. With agentic AI, the more interesting failure mode is not open rebellion. It is polite, fluent, locally reasonable drift.

The old alignment picture assumes the task stays still long enough to inspect it

Team Situation Awareness comes from research on how teams coordinate in dynamic environments. Its basic structure is simple enough to be useful: actors need aligned awareness at three levels.

First, they must perceive relevant cues. Second, they must comprehend what those cues mean. Third, they must project what is likely to happen next. Team SA extends this from individuals to teams: coordination depends not merely on each person being aware, but on team members having sufficiently compatible perception, comprehension, and projection.

For traditional human–AI collaboration, this framing maps neatly onto familiar problems. Does the human notice the AI recommendation? Does the human understand why it was made? Can the human anticipate when the system will be reliable or unreliable? Much of the literature on algorithm aversion, explainability, trust, delegation, and human oversight can be organized around one or more of these levels.

The paper’s first contribution is to say: keep the architecture, but change the object being tracked.

In bounded AI systems, the object of awareness is often a recommendation, score, classification, or next action. With agentic AI, the object becomes a moving bundle:

The phrase “same agent” deserves suspicion. In a normal organization, if a colleague changes priorities, expands authority, or starts optimizing for a different interpretation of success, someone eventually notices because human behavior is embedded in social norms, role expectations, and accountability. Agentic AI can change its trajectory, output structure, or governing logic without the same social friction. It can look consistent at the interface while becoming materially different in the workflow.

That is the first mechanism. Alignment is no longer a static match between a human goal and an AI response. It becomes sustained coherence between human intention and the agent’s evolving decision policy.

Projection congruence is the paper’s most business-relevant idea

The paper’s most practical concept is projection congruence: whether the human and AI anticipate comparable futures and assign similar weight to competing objectives over time.

This is more demanding than agreement on the current task. A manager and an AI agent may both understand that the goal is to “reduce customer support backlog.” But their projections may differ sharply. The manager may expect the agent to prioritize high-risk complaints, preserve tone quality, and escalate ambiguous cases. The agent may infer that speed is the dominant objective because previous feedback rewarded faster resolution. Both sides can agree on the words while diverging on the future.

Projection congruence asks: are the human and AI imagining the same path forward?

Not exactly the same path, of course. Perfect duplication would be useless. The point is whether their expected trajectories and value trade-offs are compatible enough to sustain coordinated action. If the agent projects a future in which aggressive automation is acceptable, while the human projects a future in which sensitive cases remain supervised, the team has not aligned. It has merely postponed the argument until operations make it expensive.

This reframes explainability. A post-hoc explanation of one output is not enough. The organization needs visibility into the agent’s anticipatory structure: what future states it is considering, how far ahead it is planning, what alternatives it discarded, what objective trade-offs it is making, and when it decides to revise the plan.

That sounds bureaucratic. It is also cheaper than discovering, three weeks later, that the agent optimized the wrong version of the workflow with great confidence and excellent formatting.

The paper’s figure and tables are conceptual scaffolding, not empirical proof

The paper does not run experiments. It is a conceptual research outlook. That matters because the reader should not treat its tables as measured effects or its framework as validated intervention design.

Figure 1 is a conceptual map. Its likely purpose is to show the continuity-and-tension structure: Team SA remains useful at the static level of perception, comprehension, and projection, but becomes strained at the dynamic level where relational trust, learning, coordination, and control operate over time.

Table 1 is a literature integration device. It maps major human–AI theory families—evaluative attitudes, relational interaction, cognitive learning, explanatory guidance, collective coordination, and operational control—onto Team SA levels. Its purpose is not to rank theories. It shows that each theory explains part of the teaming problem while leaving other levels underspecified.

Table 2 is the paper’s main framework table. It cross-classifies the three forms of open-ended agency with five domains: human SA, AI SA, relational interaction, cognitive learning, and coordination and control. Its likely purpose is to generate a research agenda by showing where Team SA can be extended and where it must be interrogated.

So the evidence here is theoretical synthesis. The business value is not “this intervention improved performance by 18%.” There is no such result, and inventing one would be rude to both science and arithmetic. The value is diagnostic: the paper gives executives, product managers, and AI governance teams a sharper vocabulary for locating where alignment may erode.

The first failure mechanism is relational fragility behind fluent collaboration

Agentic AI can make collaboration feel smoother. It responds quickly, remembers preferences, adapts to tone, and produces artifacts that look like work rather than mere suggestions. This can build trust and social presence. It can also build trust on the wrong substrate.

The paper’s relational argument is subtle. Shared awareness of the current task state does not guarantee shared expectations about initiative, authority, or appropriate autonomy. A human and an AI may both understand the goal, but disagree on what the AI is entitled to do next.

This is where the “AI teammate” metaphor becomes unstable. A teammate’s initiative is normally interpreted through role, accountability, and shared norms. If a junior analyst books a client meeting without approval, the issue is not whether the meeting agenda is well written. The issue is authority. If an AI agent does the equivalent through calendar access, CRM updates, or automated outreach, the output may be polished while the role boundary has quietly moved.

Relational fragility appears when three things happen at once:

The business mistake is to measure trust only as user willingness to rely on the system. Over-reliance can look like adoption. Under agentic conditions, the better question is whether trust remains calibrated as the agent changes plans, generates contestable representations, and reweights objectives.

Trust that cannot survive a plan revision was not trust. It was interface charm wearing a blazer.

The second failure mechanism is learning drift disguised as convergence

Team SA traditionally assumes that repeated interaction helps teams align. People compare expectations with events, correct misunderstandings, and gradually build shared mental models. That logic works when the task structure is stable enough for feedback to be corrective.

Agentic AI complicates this because learning becomes path-dependent. Intermediate outputs are not neutral. They shape what the human attends to next, what the agent treats as confirmed, and which future paths remain feasible. A small early framing error may not produce an obvious failure. It may simply narrow the search space.

This is the paper’s strongest warning for businesses deploying AI agents in complex workflows: faster convergence is not always better convergence.

An AI agent may rapidly align the human around a proposed structure. The plan feels coherent. The intermediate artifacts fit together. The workflow accelerates. Everyone enjoys the rare corporate sensation of progress. But if the initial decomposition was subtly wrong, each subsequent step can reinforce the wrong structure. The team converges quickly, but toward a distorted model of the task.

The paper calls attention to several dynamics that make this possible:

• Asynchronous updating: the AI revises its internal model faster than the human can monitor.
• Implicit commitment points: early intermediate steps constrain later options without being treated as decisions.
• Feedback endogeneity: the AI learns from human responses that were themselves shaped by prior AI outputs.
• Corrective obsolescence: a human correction that was valid under one objective configuration may become outdated after the agent’s priorities shift.

For business use, this suggests that reviewing final outputs is the wrong unit of control. The organization should review commitment points. A commitment point is a moment where the agent’s intermediate choice materially narrows future options: selecting a data source, defining a customer segment, choosing an evaluation metric, escalating or not escalating a case, calling an external tool, or adopting a particular interpretation of stakeholder intent.

The governance question is not “Was the final answer acceptable?” It is “Which intermediate assumptions became load-bearing, and who noticed?”

The third failure mechanism is oversight decoupling

The paper’s coordination-and-control section introduces the most operationally useful failure mode: oversight decoupling.

Oversight decoupling occurs when humans share awareness of task states but lose visibility into the policy logic producing those states. In plain business language: the human can see what the agent produced, but not how its decision logic is evolving.

This is dangerous because it preserves the theater of control. The dashboard still exists. The approval button still exists. The weekly review still exists. Unfortunately, the agent may have adapted its subgoals, tool choices, escalation thresholds, or objective weightings between the moments those controls were designed to inspect.

The paper argues that open-ended trajectories require staged control rather than static delegation. That is a practical design principle. Do not give an agent a broad goal and then inspect the final artifact. Define points where authority must be renewed, narrowed, or reclaimed.

For example:

This is not anti-automation. It is pro-memory. Organizations forget that authority is not a feeling. It has to be designed into the workflow.

What the paper directly shows, and what Cognaptus infers for business practice

Because the paper is conceptual, its direct claims and practical inferences should be kept separate.

This separation is important. Otherwise the article would fall into the familiar trap of turning a conceptual framework into a vendor checklist. The paper does not prove that every agent deployment needs the same oversight architecture. It does show why the old architecture—goal, output, approval—is incomplete.

A practical diagnostic for agentic AI deployments

For organizations already experimenting with AI agents, the paper can be translated into a simple diagnostic sequence.

First, identify the agent’s trajectory space. What steps can it take? Can it call tools, revise plans, create subtasks, interact with customers, update records, or delegate work? The broader the trajectory space, the less useful final-output review becomes.

Second, identify the agent’s representation space. What artifacts does it generate that humans may treat as reality? Summaries, classifications, rationales, plans, risk scores, code, customer notes, and strategy memos are not just outputs. They are cognitive infrastructure. They shape what the team believes the situation is.

Third, identify the agent’s objective regime. What is it optimizing for, explicitly or implicitly? Speed, cost reduction, customer satisfaction, legal safety, consistency, revenue conversion, or manager approval? If these objectives are not versioned and observable, the organization cannot tell whether the agent is adapting productively or drifting politely.

Finally, define projection checks. At selected stages, ask the human and the AI to state expected next states, major risks, rejected alternatives, and priority trade-offs. Compare them. The point is not to force identical answers. The point is to detect incompatible futures before they become operational facts.

A lightweight version of this can be implemented as a review template:

This is not glamorous. Most useful governance is not. It is the institutional equivalent of checking whether the bridge is still attached to both sides of the river.

The boundary: this is a research outlook, not a deployment manual

The paper’s limitation is also its strength. It is not trying to test a specific agent interface, benchmark a control design, or quantify the cost of misalignment. It is building conceptual vocabulary for a research area that is moving faster than its governance language.

That means three boundaries matter.

First, the paper focuses on the human–agentic AI dyad. This is analytically clean, but many enterprise deployments will involve multiple humans, multiple AI agents, vendors, APIs, and institutional stakeholders. Projection incongruence in those settings will not be one human versus one AI. It may occur across departments, agents, compliance rules, and platform updates.

Second, the paper identifies research questions rather than validated metrics. Projection congruence, oversight decoupling, relational fragility, and learning drift are useful concepts, but organizations still need operational measures. That is future work, not a footnote.

Third, the framework is most relevant where agents have meaningful autonomy, multi-step planning, tool access, memory, personalization, or adaptive objective handling. For a simple autocomplete feature, this machinery is excessive. Not every toaster needs a constitution.

But for AI systems that plan, act, revise, and learn inside business workflows, the paper’s warning is well aimed: alignment cannot be checked only where the work ends. It has to be maintained where the work changes shape.

The real managerial problem is not rogue AI; it is normal AI with moving assumptions

The paper’s best contribution is that it avoids melodrama. It does not need a story about AI escaping human control in cinematic fashion. The more plausible business problem is quieter: the AI remains helpful, fluent, and apparently aligned while its trajectory, representation, or objective regime drifts away from what the organization thought it had authorized.

That is why Team SA is a useful anchor but not a complete solution. Perception, comprehension, and projection still matter. In fact, they matter more. But agentic AI makes them longitudinal. The team must stay aligned not only across actors, but across time.

For business leaders, the message is uncomfortable but practical. If you deploy AI agents, you are not just installing software. You are introducing a new participant into the organization’s coordination system. That participant may generate plans, shape beliefs, alter workflows, and influence what future the team thinks it is moving toward.

So the question is not whether your AI teammate agrees with you today.

The question is whether you will notice when it starts freelancing tomorrow.

Cognaptus: Automate the Present, Incubate the Future.