From AI Adoption to Agentic Operations: What HBS’s “AI in 2026” Collection Means for Enterprise AI

HBS Working Knowledge’s “AI in 2026” research collection is framed as “From Adoption to Agentic.” Our infrastructure interpretation is a shift toward delegated execution: once AI can carry work across steps, tools, systems, and people, enterprises must decide what the system may do, with whose authority, under which controls, and with what evidence afterward.

1. The important shift is not from prompts to better prompts

Many early enterprise generative-AI programs focused on access and adoption. Agentic systems broaden the unit of work: instead of a single turn producing an answer, a run can span model calls, tools, persisted state, approvals, retries, and external side effects.

That changes the operating question. A prompt can request information or transformation; an agent run can become a delegation of work.

That is the thread connecting the HBS research. The leadership piece asks what happens when executives gain digital support teams. The jobs research asks which work people are willing to delegate. The teamwork study asks when AI improves collaborative output. The lending study asks what people care about when algorithms make consequential decisions. The talent-density discussion asks how organizations convert scarce expertise into organizational advantage.

2. HBS theme one: leadership becomes orchestration

In “What Leadership Looks Like in an Agentic AI World”, Tsedal Neeley and Ritcha Ranjan describe agentic AI as a potential digital support resource for leaders: systems that can gather information, synthesize it against organizational priorities, prepare leaders for interactions, and carry complex work semi-autonomously.

The technical implication is easy to miss. A useful “digital support team” cannot be modeled as a single stateless completion. It needs a runtime that can preserve an issue, operate over multiple turns, use tools, pause when judgment is required, and expose what happened.

This is the role an agent harness plays. TrueForge in its official documentation describes itself as an agent harness as the runtime around an LLM that runs the model/tool execution loop, manages long-task context, supports MCP tools and skills, provisions sandboxed execution when required, pauses for human approval, supports subagents, and persists sessions across reconnects and restarts.

The claim should remain bounded: TrueForge can make an agent run durable and inspectable. It does not determine a leader’s priorities, decide which tasks should be delegated, or know what good leadership looks like.

3. HBS theme two: automate tasks, not accountability

James Riley’s HBS research on public attitudes toward automation surveyed 2,357 people across 940 occupations. Respondents supported automation of roughly 30% of jobs under current capabilities; when asked to imagine AI that outperformed humans at lower cost, support rose to 58%. Yet a meaningful set of occupations remained socially or morally resistant to automation.

For enterprise architecture, the important lesson is not the headline percentage. It is that delegation boundaries are contextual. Some work can run unattended. Some work should be augmented. Some work should require a human decision even when the model is technically capable of acting.

That argues against one universal “autonomous mode.” A production system needs explicit control points:

• Which models and tools may this agent access?
• Which operations can execute without intervention?
• Which actions require human approval?
• Which downstream system remains authoritative for the final side effect?
• What happens when policy, budget, or authorization says no?

TrueFoundry MCP Gateway maps naturally to the shared tool boundary: registry and discovery, inbound identity, tool-level access control, downstream credentials, pre/post-tool guardrails, approval workflows, and audit for MCP traffic routed through the gateway. AI Gateway provides a corresponding model boundary with unified access, RBAC, rate limits, budgets, routing/fallbacks, guardrails, and observability.

Production authorization also needs to preserve distinct principals. The human or workload is the subject on whose behalf work occurs; the registered agent is the actor making the call; and the downstream service applies the credential and business authority configured for that target. TrueFoundry’s beta Agent Identity design can keep the user or service attributable while separately identifying and authorizing the calling agent. That still does not grant a business entitlement: permission to reach process_refund is different from permission to refund a particular order for a particular amount.

Neither gateway makes the underlying business decision for you. If a refund, loan, deployment, or permission change has domain-specific authorization rules, those rules still belong to the relevant policy and system of record.

4. HBS theme three: AI can become a teammate—but teamwork needs shared infrastructure

The HBS article “When AI Joins the Team, Better Ideas Surface” reports a field experiment with 791 Procter & Gamble professionals. Teams using AI produced the highest-quality solutions in the experiment; ideas in the top 10% were three times more likely to come from teams using AI than from individuals working without AI. AI also reduced completion time by 16% for individuals and 13% for teams. The final peer-reviewed study was published in Organization Science in 2026.

That does not mean every enterprise should create a swarm of agents. It suggests something subtler: AI can broaden access to expertise and help dissolve functional silos when it is embedded in a well-designed work system.

For infrastructure teams, that raises a reuse problem. If every team builds its own model adapters, tool credentials, agent loop, approval UX, skills, and traces, the organization gets local acceleration but global fragmentation.

A more scalable pattern is:

• TrueForge for the open runtime mechanics of the agent run.
• AI Gateway for shared model access and model-side policy.
• MCP Gateway for governed tool and data access.
• Skills Registry for versioned procedural knowledge that can be reused across agent surfaces.
• Agent Registry for a common inventory, access surface, metrics, and traces for registered agents, including remote agents.

The organizational benefit is not “more agents.” It is that useful practices become reusable rather than remaining trapped in one prompt, one developer’s laptop, or one team’s private integration.

5. HBS theme four: high-stakes decisions need performance and evidence

Elisabeth Paulson’s research on human versus algorithmic decision-makers studied approximately 9,000 participants. In the loan scenario, participants were 4.3 percentage points more likely to choose a human bank manager than an algorithm. Participants still showed an average preference for human decision-makers, but the study found that the performance attributes of the decision maker collectively mattered more than whether it was human or algorithmic.

That is not evidence that fairness, legality, or due process can be traded away. The study itself evaluates stated preferences in two conjoint experiments, not the real-world validity of deploying an automated decision system. The stronger systems lesson is narrower: performance claims need measurable evidence, while legal, ethical, privacy, fairness, transparency, and implementation obligations must be assessed independently.

That requires separating at least four layers:

TrueForge’s Agent → Session → Turn → Event model can preserve runtime evidence around a run. TrueFoundry Gateways add request and tool-boundary telemetry for traffic routed through them. Those records are useful substrates for debugging, review, evaluation, and audit—but a trace is not itself an evaluator, and an approval is not a substitute for authorization.

6. HBS theme five: talent density creates a reuse problem

In HBS Working Knowledge’s interview with Boris Groysberg, the central argument is that acquiring top AI talent is easier than integrating that talent so its knowledge compounds across an organization. “Talent density” is not only about the number of exceptional people; it is about putting strong people in critical roles and creating conditions where their expertise changes the performance of the system around them.

That research creates an infrastructure implication. Highly capable AI engineers should spend their scarce time on architecture, evaluation, product judgment, model/tool choice, and the distinctive logic of the business—not rebuilding authentication, provider adapters, tool credential handling, approval state, cost accounting, and trace plumbing for every application.

TrueFoundry Skills Registry is particularly relevant here. It provides a shared catalog of reusable, versioned Agent Skills, with repository RBAC, version history, and audit. That turns a useful procedure from an individual technique into an organizational artifact. The same principle applies to registered agents, gateway policies, routing rules, budgets, and shared observability.

7. From organizational findings to an agentic operating model

Putting the five HBS themes together suggests an enterprise operating model that is neither “humans approve everything” nor “agents run everything.” It is a layered delegation system:

8. What should enterprises standardize now?

The HBS research is organizational, not a software architecture specification. But once an organization accepts the premise that AI will increasingly operate as a teammate, delegate, and decision participant, a set of infrastructure questions becomes unavoidable.

1. Standardize the agent runtime. Define how sessions persist, tools are discovered, context is managed, code is isolated, approvals pause execution, and events are recorded.
2. Separate model policy from application code. Centralize provider credentials, access control, routing, budgets, rate limits, guardrails, and model telemetry where possible.
3. Put enterprise tools behind explicit identity and policy. Preserve the human or workload subject, identify the agent actor, and enforce downstream business authorization. A tool does not become safe merely because it speaks MCP.
4. Design HITL as a scoped runtime state transition. The run should pause before the side effect, preserve the exact pending action, record the reviewer decision, and resume from persisted state. Approvals should expire or be revalidated after material state changes.
5. Preserve outcome evidence. Runtime traces explain what the agent attempted; systems of record prove what actually happened.
6. Turn good procedures into reusable artifacts. Skills, policies, agent definitions, and evals should be versioned rather than living in copied prompts.
7. Keep business accountability outside the model. Human owners and authoritative systems still decide what matters, what is allowed, and what counts as success.

A concrete control path: processing a refund

Consider a support agent using the get_order and process_refund tools in the TrueForge documentation. A production path starts by persisting the customer request, session, and identity context. The agent may retrieve the order through MCP Gateway, but tool reachability is only the first check: the application or order system must still confirm that this customer—or the service acting for the customer—may access that order and that the order is eligible for a refund.

If the model proposes process_refund, the runtime should create a pending action before any mutation. That record should bind the approval to the exact order, amount, reason, operation ID, and relevant policy or data version. A reviewer approves that specific action, not the tool in the abstract and not every future argument set. If the order changes materially while the run is paused, the action should be revalidated or sent back for approval.

After approval, TrueForge can continue the chained session from persisted state. The mutating request should carry an idempotency key or stable operation ID. If the connection times out, the system should not assume failure and blindly replay the refund; it should reconcile the operation against the authoritative order or payments system, then retry only when the recorded outcome permits it. This distinction separates the agent decided to attempt a refund, the tool call was emitted, and the refund actually committed.

TrueForge events and Gateway traces can correlate the proposal, policy checks, approval, call, response, latency, and cost. The system-of-record receipt remains the authoritative outcome evidence. Evaluators can then operate over the joined record—offline regression sets, trajectory or task-success checks, and post-deployment outcome monitoring—without pretending that tracing itself establishes quality. The platform value is the coherence of these boundaries: runtime state, model policy, tool policy, identity, approval, and evidence stay connected while business authorization and final truth remain with the domain system.

9. The deeper point: agentic AI is an organizational systems problem

The phrase “from adoption to agentic” is useful because it marks a real change in responsibility. When AI is a tool, the human performs the work and AI assists. When AI becomes agentic, part of the work is delegated to a system that can choose intermediate steps and interact with external capabilities.

That is why enterprise agent architecture cannot stop at model selection. The organization needs a runtime for the delegated work and control planes around the resources that work can reach.

TrueForge is TrueFoundry’s open-source agent harness for this runtime layer. AI Gateway, MCP Gateway, Agent Registry, and Skills Registry provide shared control and reuse surfaces around models, tools, agents, and procedural knowledge. None replaces organizational judgment. Together, they provide infrastructure for encoding policy, enforcing access, preserving execution evidence, and improving agent behavior over time.

References

1. HBS Working Knowledge — What Leadership Looks Like in an Agentic AI World.
2. HBS Working Knowledge — People Are Mostly OK With AI Taking Over Many Jobs—Up to a Point.
3. HBS Working Knowledge — When AI Joins the Team, Better Ideas Surface.
4. Organization Science — The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork.
5. HBS Working Knowledge — Who Should Approve Bank Loans: People or Algorithms?.
6. PNAS Nexus — Public Attitudes on Performance for Algorithmic and Human Decision-Makers.
7. HBS Working Knowledge — Winning with AI Starts With 'Talent Density'.
8. TrueForge — Introduction.
9. TrueForge — SDK Concepts.
10. TrueForge — Sandbox.
11. TrueFoundry — Introduction to AI Gateway.
12. TrueFoundry — MCP Gateway.
13. TrueFoundry — MCP Gateway Authentication and Security.
14. TrueFoundry — Agent Identity (beta).
15. TrueFoundry — Agent Registry.
16. TrueFoundry — Skills Registry.

Editorial disclosure: Harvard Business School, HBS Working Knowledge, and the featured faculty are not affiliated with this article and do not endorse TrueFoundry. HBS research is used as an external framework; all product mappings and infrastructure conclusions are TrueFoundry editorial analysis. Product capabilities are described from public documentation available as of September 1, 2026.

‍