Different Strengths, Shared Potential

Reasoning models, in our view, are most effective when the desired output is analysis or judgment — executive memos, plans, risk reviews or QBR narratives. They’re relatively simple to pilot since they draw on existing documents or curated data sources and they allow for predictable cost and human review before decisions are finalized. Their limits are just as important to understand. Without strong grounding in company data and policies, reasoning models can produce confident but inaccurate conclusions. They also can lack persistence across interactions unless you design a memory layer, and while they can use tools, they don’t act autonomously without orchestration.

Agentic AI tends to excel when the outcome requires consistent, cross-system execution. Agents are typically well-suited to operational workflows such as lead enrichment and follow-ups, financial reconciliation, entitlement checks or support triage. With proper instrumentation, agents can run continuously, escalate low-confidence cases and deliver measurable throughput. But they come with trade-offs: increased security and maintenance overhead, dependency on clean data and clear rules, and potential latency or cost as action chains grow more complex. Importantly, agents need explicit product ownership to ensure playbooks stay current as processes and policies evolve.

In practice, many companies benefit from pairing the two. If the goal is a document, plan or recommendation that benefits from human review, start with a reasoning model. If the goal is measurable progress in a defined workflow, such as closing renewals on time, resolving tickets within an SLA, or reconciling financials daily, an agent is the better fit. Combining both models can be powerful as they bring together structured reasoning and coordinated action: the reasoning model generates a transparent plan — who to contact, what to say, which fields to update — and the agent executes it, escalating to a human when confidence is low or when actions are high risk.

Building, Buying and Partnering Wisely

In our view, the default posture for most growth companies should be to buy the platform foundations and build the differentiating layers. We believe this approach balances efficiency, value and control, allowing teams to move quickly while retaining ownership over what truly sets the company apart.

Here are our recommendations:

• Build when the workflow is core to differentiation and your proprietary data, nuanced playbooks or decision logic help define your competitive advantage. Building can also make sense when the workflow is subject to strict regulatory or data-storage requirements, when deployment must happen in your own environment for policy control, or when sustained volume justifies owning unit economics and roadmap. These are the layers that we believe warrant continued investment over time.
• Buy the elements that are standard within the industry (e.g. grounding, redaction, observability, orchestration, agent policy), especially when the vendor already integrates with your core systems. Buying a ready-made product provides security, admin controls and cost transparency out of the box, reducing the need for internal support and maintenance. Start with the built-in settings and then extend them only where your business requires customization.

Vendor diligence, when buying, should center on four areas:

1. Data policy: retention, training use, residency and isolation
2. Security and deployment: SSO, SCIM, audit depth and private or tenant options
3. Observability and control: metrics for cost, latency and errors plus approval gating
4. Portability and pricing: bring-your-own-model options, log exports and transparent billing

Striking the balance between buying for stability and building for differentiation can help companies accelerate adoption while maintaining flexibility and control.