They’re employees, not endpoints: A labor‑law playbook for managing digital colleagues
Written by: Peter Wahlgren, Jens Eriksvik, Alex Ekdahl
The most disruptive labor transformation in decades isn’t happening in HR. It’s happening in code. In barely six years the “brain size” of frontier language models has exploded while the meter to run them has gone into free‑fall. GPT‑2’s 1.5 billion parameters in 2019 [1] now look quaint next to GPT‑4’s estimated 1.8 trillion [2], with credible roadmaps for GPT‑5 in the 3‑5 trillion range [3], roughly a 1000x jump in raw model capacity. Meanwhile, the cost of letting that capacity loose on real work has collapsed: researchers find the per‑token price of GPT‑4‑level performance sinking by 40x each year [4], and OpenAI’s new GPT‑4.1 API launched in April at 75 % lower rates than last year’s offering [5]. When capability rockets upward and price plummets this fast, “experimental chatbots” morph into full‑time digital colleagues almost overnight, raising every labor, liability, and governance question you normally reserve for humans.
As explored in Algorithma’s article on the collapse of enterprise software logic, the shift away from platforms toward protocols and agents is not just technical, it’s operational and legal. In this new model, who acts and who’s accountable are no longer the same person.[6]
"When autonomous systems perform tasks once reserved for people, the legal architecture of work itself begins to shift. AI agents don’t just raise new questions, they rewrite the conditions under which questions about responsibility, intent, and control are asked."
- Peter Wahlgren, Managing Partner at Algorithma
Just after midnight on April 30 ‑ May 1, 2025, when US markets were whipsawing on fresh tariff news, JP Morgan’s private‑bank advisers handled a surge of panicked client calls. Most of the heavy lifting was done by Coach AI, a large‑language‑model “associate analyst” that pre‑pulls research, drafts talking points, and personalizes advice. Advisers say the system now surfaces answers 95 % faster and helped lift gross sales 20 % year‑on‑year, all while the human team slept.[7]
JP Morgan is not an outlier. Salesforce calls its new Agentforce layer a “digital workforce,” Microsoft’s 2025 Work Trend Index casts managers as “agent bosses,” and McKinsey finds that only 1 % of companies feel “mature” at integrating these autonomous coworkers despite betting trillions of dollars on them. Across sectors, from call‑center routing to pension‑file triage, algorithms now hold real job titles, access sensitive data, and interact with customers around the clock. As Algorithma noted in its 2025 analysis of hybrid enterprise operating models, these AI agents are no longer isolated pilots, they’re embedded actors in daily operations, with growing spans of responsibility and shrinking human oversight.
Yet most of these digital colleagues arrive with no employee file, no Code of Conduct, and no line manager on record. They’re treated like software patches rather than staff who can act, err, and incur liability. If a flesh‑and‑blood junior banker mis-quotes a client or an intern leaks personal data, HR and Legal have clear playbooks. When an AI agent does the same in the middle of the night the chain of accountability vanishes.
That gap is a risk, and an historic opportunity. Legal departments must stop bolting tech‑governance checklists onto engineering roadmaps and instead draft a labor‑style framework for digital colleagues: job descriptions, onboarding, probation, supervision, due‑process, even termination. Until those policies exist, every new agent your company hires is a weekend shift with no badge, no boss, and no insurance.
Onboarding and job descriptions: Write the employee file first, code second
Like humans arriving for their first day, every digital colleague requires a clear definition of its role and place within the organization. Treating these agents merely as software to be installed misses the fundamental point that they will be performing tasks that carry responsibility and risk, just like human employees.
Like humans:
Every employee starts with a formal role definition: a job title, specific responsibilities, a reporting line, defined KPIs, and granted system access.
They undergo essential background checks and credential verification before being granted access to sensitive data or critical systems.
They receive structured onboarding, policy training, and are formally placed within the organizational chart, regardless of whether they are temporary or junior staff.
For AI agents:
Treat your digital colleagues no differently in principle. If an agent is assigned tasks like drafting contracts, flagging compliance risks, handling customer financial data, or triggering critical workflows, it demands the legal and operational equivalent of a formal employee file.
Start by creating a digital colleague charter that documents:
Its mandate and the boundaries of its tasks.
Its specific data entitlements and required permissions.
Defined escalation paths and fallback logic for scenarios it cannot handle.
Clear criteria for tracking its performance, performance leading to opportunities for larger responsibility or performance issues resulting in scope reductions or decommissioning
A named human supervisor or team of record who is accountable for overseeing the agent's actions and outputs.
And just as HR screens people before granting access to the building or sensitive information, AI agents require the equivalent rigorous skills and ethics checks before deployment:
Model evaluations to confirm technical fitness for the assigned tasks. In AI model evaluation: bridging technical metrics and business impact, we argued that technical accuracy alone is insufficient for enterprise deployment. Evaluations must address reliability under uncertainty, escalation behavior, and downstream impact, especially for agents tasked with decisions in sensitive or regulated domains. [8]
Bias audits to ensure fairness and alignment with internal values and anti-discrimination requirements.
Embedded guardrails to automatically block unauthorized or out-of-scope actions.
These documented steps and checks are not merely technical configurations or governance "nice-to-haves." From a legal and operational standpoint, they are fundamental preconditions for assigning real responsibility and managing foreseeable risks.
“A model card is the resume your algorithm hands to HR.” - IBM AI Governance Lead, April 2025
From a legal standpoint, approaching AI agents through formal onboarding is important for risk containment. Assigning significant tasks to an AI agent without a clearly defined charter risks breaching your duty of care if harm occurs that was foreseeable. Without clear scope, defined oversight, and traceable actions, the attribution of liability becomes inherently murky, the enforcement of internal policies weakens significantly, and contractual clarity with third parties regarding agent interactions erodes. Regulators, from the EU's AI Office to national data protection authorities and sector-specific bodies, are emphasizing the need for documented governance-by-design, which includes traceable roles, defined permissions, and clear accountability structures for AI systems.
In our framework for hybrid AI-human enterprises, onboarding is not a technical checklist, it’s a fundamental policy act. Assigning a span of responsibility to an AI agent without a formal charter that acts as its "job description" and "contract" is functionally equivalent to hiring a contractor with no written agreement, no designated supervisor, and no defined red lines. It doesn’t just weaken internal governance or make operations messy. Fundamentally, it invites significant and potentially uninsured liability.
"We used to think of systems as tools. But once they start acting, deciding, escalating, even improvising, they stop being just tools. They become teammates. And that means we have to rethink everything from architecture to accountability."
- Alex Ekdahl, Senior AI leader
Once a digital colleague has been formally onboarded, received its charter, defining its role and initial mandate, and passed its pre-hire checks, the critical challenge shifts to ensuring its ongoing conduct adheres to the vast and complex rulebook governing human employees. Compliance for your digital workforce is not merely a technical configuration; it’s establishing their adherence to workplace law and company policy as they perform their assigned tasks.
Agents need the same rulebook as people, plus a few pages
Just as every human employee must understand and follow company policies and external regulations, your digital colleagues operate within a dense web of compliance requirements that govern data handling, decision-making fairness, transparency, and more. Non-compliance, whether by human or algorithm, carries significant legal, financial, and reputational penalties.
Like humans:
Staff undergo mandatory, regular training covering everything from data privacy laws (like GDPR, CCPA, etc.) and anti-discrimination statutes to industry-specific regulations, ethical guidelines, and the company's internal Code of Conduct.
Adherence to these rules is expected in all daily work activities.
Workplace misconduct, whether intentional or accidental, triggers formal investigations, potentially leading to warnings, corrective action, suspension, or even dismissal, depending on the severity and impact.
For AI agents:
The digital equivalent of "mandatory training" means embedding regulatory constraints, internal policies, and ethical guardrails directly into the agent's design, configuration, and operational parameters as hard policy guards and rule sets. This is the digital equivalent of mandatory, continuous training.
Adherence must be actively monitored. Every "decision shift," action that deviates from expected behavior, or outcome that triggers a risk flag must be automatically logged and subject to review, much like logging performance issues or potential misconduct incidents for human staff.
Consider implementing a dynamic compliance score or risk rating for each agent, updated regularly during their "review cycles" to reflect ongoing adherence, error rates, and performance against policy. This helps identify agents needing "re-training" (reconfiguration) or potentially "termination" (decommissioning).
From a legal standpoint, passive compliance via system audits is insufficient. Given AI agents' capacity for autonomous action, compliance must be active and embedded [9]. Failure to ensure agents operate within legal and regulatory boundaries, especially concerning data privacy, non-discrimination, and consumer protection, exposes the firm to direct liability, hefty fines, and mandatory operational halts. Legal teams must define the policy guards required and ensure traceability allows for demonstrating compliance to regulators and investigating incidents effectively.
Consider e.g. Air Canada [10] in the case where it was found liable for bad travelling advice from its chatbot: "It establishes a common sense principle: If you are handing over part of your business to AI, you are responsible for what it does," Gabor Lukacs, president of the Air Passenger Rights consumer advocacy, said. "What this decision confirms is that airlines cannot hide behind chatbots."
Leading firms and regulatory bodies emphasize AI governance. For instance, the EU AI Act places stringent obligations on deployers of high-risk AI systems, requiring robust quality management, risk management, data governance, documentation, and human oversight – effectively mandating a formal compliance framework that in certain aspect would be similar to safety-critical human roles (remember the section headline: “plus a few pages” 🙂). Building compliance at the agent level is the only scalable way to meet these evolving expectations.
Treat errors like professional negligence, not system bugs
Ensuring digital colleagues operate within defined compliance boundaries is essential. But like human employees, even rule-aware AI agents can make mistakes within the scope of their assigned work. Managing those instances, assessing performance, responding to incidents, and accepting accountability, is a cornerstone of the AI-native labor framework.
We must shift away from treating agent failures as technical defects. Instead, we should recognize them as potential cases of professional negligence: lapses in duty by a system entrusted with responsibility. This reframing moves AI governance from engineering silos into the domains of Legal, Risk, and HR, where it belongs.
Like humans:
Employees are assigned a defined span of control. They are expected to act autonomously within this boundary and escalate situations that exceed their expertise or authority.
If an employee acts negligently within their role and causes harm, responsibility may initially fall on the individual, but ultimate liability lies with the firm (vicarious liability).
Recurring issues are addressed through structured performance management: monitoring, review, retraining, escalation, or dismissal.
For AI agents:
Each agent must have a clearly defined span of responsibility, a documented scope of tasks and decisions it is authorized to “own,” as outlined in its charter.
Within that span, assign a duty of care: what “reasonable” behavior looks like for this role, given the risks, expectations, and data environment.
Design explicit escalation protocols, risk thresholds or uncertainty triggers that prompt handoff to a human.
Implement supervisor-of-record mapping: every active agent is paired with a named human, line manager, product owner, or risk lead, responsible for reviewing outputs and ensuring accountability.
Create AI incident playbooks that mirror HR misconduct workflows: Detection, Investigation (review logs, data, decision path), Corrective action (guardrail update, prompt rewrite, escalation), retraining (fine-tuning) or termination (kill switch).
From a legal perspective, assigning operational tasks to autonomous AI agents creates novel liability challenges. Traditional legal tests for fault and causation, built for humans or deterministic systems, don’t map cleanly onto probabilistic agents. However, the absence of governance makes this worse.
In our analysis of hybrid labor models, the key to managing AI agent liability isn’t rewriting law, it’s importing principles from employment law. When AI agents have defined roles, human supervisors, and codified duties of care, firms gain the tools to investigate failures, assign responsibility, and respond with appropriate measures, just as they do with human staff. [11]
By formally defining span of responsibility, duty of care, and supervisory oversight, firms establish a traceable chain of accountability. This doesn’t eliminate liability, but it turns amorphous risk into structured, defensible, and potentially insurable exposure. It allows the legal function to demonstrate that it met its obligations in agent design, deployment, and oversight.
Framing agent errors as software bugs limits accountability to IT. But reframing those errors as operational negligence or misconduct brings oversight under the correct lens, enterprise risk. This elevates responsibility to legal, compliance, and business leadership, and ensures failures are reviewed with the same rigor applied to human performance breakdowns.
This is not just about fairness. It’s about institutional survival in an economy where AI doesn’t just assist, it acts.
Having explored the critical need to treat AI agents as digital colleagues subject to workplace-like governance – from onboarding and compliance to performance and liability – the question shifts to practical implementation. How can Legal leaders begin constructing this essential labor framework for algorithms now?
Build your AI-native labor framework now
Building a robust legal framework for your digital workforce requires a proactive, strategic approach that mirrors the policy structures already in place for human employees. Here are five key moves Legal leaders should prioritize to start shaping the AI-native enterprise responsibly:
These five moves represent the foundational elements of an AI-native labor framework. By focusing on these practical deliverables, Legal can begin translating the necessary governance principles into tangible policies and processes that integrate digital colleagues into the organizational structure safely and accountably.
Your next labor dispute could involve zero people
The era of digital colleagues is not a distant future; it is here, operating within your enterprise workflows right now, often without a formal reporting structure or a clearly defined rulebook. As AI agents take on roles traditionally performed by humans, the most pressing task for Legal is to stop treating them as mere technical endpoints and start managing them as the employees they functionally are.
"If digital colleagues carry real responsibility, they also carry real risk. Treating them like software won’t cut it. For businesses, the ability to scale AI now depends less on what the models can do, and more on whether the organization is ready to govern them."
- Jens Eriksvik, CEO, Algorithma
Ignoring this shift introduces silent, compounding risks: unmanaged liability when agents err, unforeseen compliance breaches, and operational chaos from undefined responsibilities. The old playbook, designed for human workforces and static software, is inadequate for the dynamic, autonomous nature of AI agents operating in a protocol-driven enterprise.
The quickest way to derail the immense potential of AI adoption and the productivity dividend it promises is to keep these powerful agents operating in a legal and governance shadow. Instead, Legal must champion the creation of a labor-style framework for your digital colleagues [12]. Bring them onto the organizational chart conceptually, hand them an employee handbook in the form of defined charters and embedded policies, and have legal actively enforce this new reality.
By defining roles, establishing clear supervision, embedding compliance directly into agent design, and building HR-like processes for performance and incident management, you don't just mitigate risk, you build trust with customers and partners, ensure regulatory adherence, and create the necessary conditions to scale AI responsibly and outperform less structured competitors.
As one expert put it, regarding the rapid deployment of agentic AI: "We have laws. But without adaptation, we have liability without clarity." The AI-native enterprise will happen. The future of work is hybrid. Now is the time for Legal to lead the transition, define the rules, and make this hybrid workforce legally coherent and accountable. Get in front. Write the framework. Shape the future of work.
And as a final note, let’s be clear: AI agents aren’t people. They don’t need coffee breaks, birthday cards, or union reps. But they do make decisions, handle sensitive data, and operate inside our businesses in ways that carry real consequences. We know the legal frameworks for humans and algorithms aren’t the same, and they shouldn’t be. But pretending agents are just ‘tools’ is starting to look less like caution and more like denial. This isn’t a manifesto. It’s an invitation. We’re asking the legal community to step in, not when it’s safe and settled, but now, when the rules are still being written.
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Algorithma’s October 2024 insight, “Beyond deployment: Embracing AI sustainment for lasting value,” emphasizes that legal and compliance obligations cannot be layered on post-hoc, they must be embedded from design through sustainment. This requires an operational model that treats agent compliance as a continuous business function, not a one-off audit
This echoes Algorithma’s cross-sector advisory experience, where legal and risk leaders increasingly take the lead in defining governance-by-design policies for AI agents embedded in finance, customer operations, and product functions.
