Planview, the leading end-to-end platform for Strategic Portfolio Management (SPM) and Digital Product Delivery (DPD), announced new agent resource management capabilities, delivering enterprises a single system to plan, predict, govern, and optimize both human and AI agent resources across their portfolio of strategic investments. With these new capabilities, leaders can see who — and what — is doing the work, what costs are associated, confirm work is aligned to the right priorities, and hold every action accountable to a human decision-maker.

By the end of 2026, Gartner predicts 40% of enterprise applications will include integrated task-specific AI agents, a 700% increase from 2025¹. Additionally, Deloitte’s Q4 2025 CFO Signals Survey found 54% of CFOs prioritizing AI agent integration in 2026².

“Agentic AI has reshaped how businesses get work done. Enterprises now run blended human and agent workforces, but most leaders lack insight into whether those resources are allocated to the right work and who is accountable when something goes wrong,” said Matt Zilli, CEO, Planview. “Planview is giving leaders the control they need to run a blended workforce with confidence and to answer the questions that every executive team is asking: what is agentic AI actually costing us — and is it delivering the outcomes we need?”

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Agent Resource Management: One System to Manage Entire Workforce

Agent resource management extends the Planview resource model, already trusted by enterprises to manage human capacity across portfolios, to include AI agents and their total cost. Human resources and AI agents appear side-by-side at assignment, so resource managers see the true cost of every capacity decision before they make it and know who assigned the work. Planview’s agent resource management capabilities give every enterprise the visibility and governance a blended workforce demands, including:

• Know — see human resources, AI agents, and their full associated costs (including compute and token spend) in one view, attributed to specific work and strategic initiatives.
• Plan and predict — model resource scenarios that include AI agents alongside people, understand staff morale, flag capacity risk, and forecast the return on investment of different human-to-agent mixes before committing.
• Allocate and assign — deploy AI agents to portfolio work with defined authority boundaries, budget ceilings, and escalation paths set at the moment of assignment.
• Track and govern — keep a continuous audit trail of agent actions, human approvals, and policy events. Enforce policy at runtime to prevent AI agent actions that exceed their boundaries. These actions are halted before they commit, with automatic escalation to the accountable human.
• Optimize — run substitution modeling across the portfolio, with insights into real-time data, continuously, as priorities change.

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Purpose-Built AI Agents, Governed from Day One

Planview’s agent resource management ships with purpose-built AI agents designed for high-friction roles in portfolio delivery — not as experiments, but as assignable, cost-tracked resources governed alongside human resources.

• The PM Agent. Delivers status updates, flags blockers, and generates standup updates on demand.
• The Backlog Agent. Runs a “definition of ready” check on every card it’s assigned — scoring description clarity, acceptance criteria, estimates, and blocker risk — and drafts missing content directly into the card for human review.
• Forecasting Agents. Flag risks based on delivery confidence, work complexity, and historical velocity, giving leaders early warning rather than late surprises.

“Planview has managed enterprise resource capacity longer and at a greater scale than any other portfolio management vendor,” said Louise Allen, Planview Chief Product Officer. “That experience — how resources are actually allocated, how blended workforces operate in practice, how agent capacity should be governed — defines our agent resource management. No horizontal AI platform offers this combination of resource management history, portfolio context, and enterprise trust.”