Agentic GenAI: Where We Are Going

The next frontier is outcome ownership: agents will be managed, measured, governed, and bought as work capacity.

The near-future direction of agentic GenAI is a shift from software access to business outcomes. Bain’s advice to SaaS leaders – price for outcomes, not log-ons – captures the commercial pressure, while McKinsey describes the rise of “service-as-software,” where platforms, AI agents, automation, and expert support are bundled into outcome-oriented solutions. Sierra’s outcome-based pricing model for AI agents is an early commercial pattern: pay when the agent resolves, completes, saves, or converts. In boardroom terms, the unit of value is moving from seat, API call, or subscription to “resolved incident,” “completed migration,” “reconciled account,” or “qualified customer.” [1][2][3]

The infrastructure layer is moving in the same direction. Claude Managed Agents turns the agent loop, tools, runtime, events, and execution environment into managed infrastructure for long-running asynchronous work. OpenAI’s Agents SDK and Responses API similarly push developers toward controlled workspaces, shell tools, hosted containers, and model-native harnesses. Cursor’s self-hosted cloud agents show the enterprise version of the same idea: agents get their own development environments, but the customer can keep code, artifacts, and execution inside controlled infrastructure. [4][5][6][7]

Verification and memory will become first-class product features. Claude Managed Agents “Outcomes” lets teams define what “done” means with a rubric, then uses a separate grader context to evaluate artifacts and return feedback to the agent. Multiagent sessions let a coordinator delegate to agents with isolated context, while “Dreams” introduces a research-preview memory consolidation pattern: reading prior sessions and memory stores to merge duplicates, remove stale entries, and surface new insights. This is the operational future of agents: not one long chat, but a lifecycle of planning, execution, verification, memory consolidation, and continuous improvement. [8][9][10]

Interfaces will also invert. Instead of humans navigating software while the model comments from the side, agents will increasingly operate inside agent-native surfaces where tools return live UI components and humans review, approve, or steer. MCP Apps standardizes interactive interfaces over MCP, and spec-driven development frameworks such as GitHub Spec Kit, OpenSpec, Kiro, and BMAD point toward a world where requirements, plans, implementation tasks, and acceptance criteria become executable artifacts. The technical curiosity here is subtle but important: the “spec” becomes part product brief, part test oracle, part project manager, and part memory anchor for future agents. [11][12][13][14][15][16]

Strategically, this means the marginal value of code will decline while the value of proprietary workflows, trusted data, evaluation suites, and governance will rise. Anthropic’s eval guidance makes the key point: once agents scale, teams need traces, outcomes, evaluation harnesses, and production monitoring – not intuition. Its trustworthy-agent framing is equally relevant for executives: the more autonomy agents gain, the more important human control, privacy, transparency, and secure interaction become. The companies that win will not be the ones that merely “use agents.” They will be the ones that redesign work around verifiable delegation. [17][18]

The organizations that will capture the most value from this shift are not the ones that move fastest, but the ones that build the right foundations before they scale. Verifiable delegation requires knowing what you are delegating, to what standard, and with what safeguards – and most organizations have not yet answered those questions systematically. Our AI Readiness Assessment is designed for exactly this moment: it maps your maturity across the six dimensions – technology, data, skills, security, governance, and culture – that determine whether agentic workloads succeed or stall, and it delivers a prioritized roadmap rather than a generic recommendation. For teams already past the strategy phase and ready to put an agent to work on live business data, BRIAN embodies the outcome-oriented, infrastructure-controlled model this article describes: configurable permissions, auditable reasoning, and decision-ready outputs that stay inside your own environment. The future of work is verifiable delegation. The question is whether your organization is ready to delegate.

References

• [1] Will Agentic AI Disrupt SaaS?  –  Bain & Company  –  https://www.bain.com/insights/will-agentic-ai-disrupt-saas-technology-report-2025/
• [2] Seven shifts to become AI-centric in software  –  McKinsey  –  https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-ai-centric-imperative-navigating-the-next-software-frontier
• [3] Outcome-based pricing for AI agents  –  Sierra  –  https://sierra.ai/blog/outcome-based-pricing-for-ai-agents
• [4] Claude Managed Agents overview  –  Anthropic Claude API Docs  –  https://platform.claude.com/docs/en/managed-agents/overview
• [5] Define outcomes  –  Anthropic Claude API Docs  –  https://platform.claude.com/docs/en/managed-agents/define-outcomes
• [6] Multiagent sessions  –  Anthropic Claude API Docs  –  https://platform.claude.com/docs/en/managed-agents/multi-agent
• [7] Dreams  –  Anthropic Claude API Docs  –  https://platform.claude.com/docs/en/managed-agents/dreams
• [8] The next evolution of the Agents SDK  –  OpenAI  –  https://openai.com/index/the-next-evolution-of-the-agents-sdk/
• [9] From model to agent: Equipping the Responses API with a computer environment  –  OpenAI  –  https://openai.com/index/equip-responses-api-computer-environment/
• [10] Run cloud agents in your own infrastructure  –  Cursor  –  https://cursor.com/blog/self-hosted-cloud-agents
• [11] MCP Apps: Bringing UI Capabilities to MCP Clients  –  Model Context Protocol Blog  –  https://blog.modelcontextprotocol.io/posts/2026-01-26-mcp-apps/
• [12] SEP-1865: MCP Apps  –  Interactive User Interfaces for MCP  –  Model Context Protocol  –  https://modelcontextprotocol.io/seps/1865-mcp-apps-interactive-user-interfaces-for-mcp
• [13] Spec-driven development with AI: Get started with a new open source toolkit  –  GitHub Blog  –  https://github.blog/ai-and-ml/generative-ai/spec-driven-development-with-ai-get-started-with-a-new-open-source-toolkit/
• [14] OpenSpec  –  https://openspec.dev/
• [15] Kiro Documentation  –  AWS  –  https://aws.amazon.com/documentation-overview/kiro/
• [16] BMAD Method Documentation  –  https://docs.bmad-method.org/
• [17] Demystifying evals for AI agents  –  Anthropic  –  https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents
• [18] Trustworthy agents in practice  –  Anthropic  –  https://www.anthropic.com/research/trustworthy-agents