Managing Agents and Integrations: Build, Buy, or Use AI?

Many organizations are deploying agents before putting the machinery in place to supervise them. In a recent Omdia study of more than 1,100 senior decision-makers, while 74% already have active AI initiatives, fewer than half (46%) run integration through a platform.

That disconnect between what’s being rolled out and what’s holding it up is where most agentic programs come undone. If your agents remain ungoverned, you can’t answer three critical questions: what they’re reaching, what they’re costing, and what they’re getting wrong.

To make sure your AI operations run safely, truthfully, and reliably, you need an agentic AI data foundation with strong agent management underneath, whether you build, buy, or use AI agents.

What Agentic AI Actually Needs in Enterprise Integration

Think about AI agents as software with an objective. When you tell them what you want to achieve, they reason about how to reach it and then act, rather than waiting for you to specify each step. In an integration context, that means generating and running the pipelines themselves, calling your APIs as tools, orchestrating multi-step workflows, and investigating failures.

It’s also important to remember that agents are non-deterministic, so if handed the same task twice, they may take two different routes to reach their goal.

What’s more, while one of the biggest boons of agents is the promise of fast results, this speed is a double-edged sword because it accelerates and multiplies outcomes based on your data, whether that data is good or bad.

As Steve Lucas, chief executive officer of Boomi, said at Boomi World 2026: “If you are struggling to deliver data to human intelligence in real time with quality, how do you think that’s going to play out for AI that operates thousands of times faster?”

With ungoverned AI, you get slop: plausible but unvalidated and produced faster than anyone can review. Plus, there’s no reuse, because every request generates fresh artifacts outside your catalogs. Spend scales by orders of magnitude, since agentic workloads burn far more tokens per task than chat does. And teams end up solving the same problems in different ways, unaware that other solutions already exist.

The right solution for governing agents isn’t just a matter of balancing speed with control; it also requires grounding agents in real business context before harnessing their creativity.

Beyond the model you pick, you need to consider the following:

Efficient Context Windows

A model’s window is finite, and every token in it is billed. Longer windows tempt teams to paste in whole document sets on the theory that more context can’t hurt, which inflates cost per call at agentic volumes.

The solution is to keep the authoritative material in a knowledge base and retrieve exactly the slice you need. Your agent then pays only for the context it uses rather than the context someone thought it might have wanted. That also turns context into something you can govern: one endorsed source your agents draw from, with a record of what was retrieved on any given call, rather than a prompt nobody can reconstruct after the fact.

Congruent Business Context

Agentic AI for data quality falls apart the moment two teams disagree about what a field means. Definitions surviving only in people’s heads cost your staff time and cost your agents accuracy. Maybe your sales and operations teams both say “active customer” and mean different things by it, and there’s no record of which one wins. A human can pause at a number that looks odd and ask someone who would know, but your agent doesn’t pause; it just believes what it’s told and works away until it finishes.

To remedy these issues, you’ll need semantic metadata and endorsed glossaries: definitions your experts have formally reviewed and linked to the schemas, connectors, and agents they describe.

Robust Security Policy

An agent armed with a broad credential will use every permission it is given. So, a single over-scoped authentication method can turn one bad instruction into thousands of unauthorized actions.

Guardrails belong in the agent rather than the prompt. Access to data and APIs should be role-based and policy-driven. The tools agents call need quotas and rate limits, and consequential actions must have human approval. Start agents on read-heavy APIs and extend autonomy based on real-world business cases.

Integration Tools: Build vs. Buy vs. Use AI?

So, should you build, buy, or use AI? The truth is most enterprises are already running all three paths without having explicitly decided to. Maybe you bought an integration platform, someone in finance built a custom reconciliation script nobody has looked at since, and a developer has been generating connectors with a coding assistant for months.

The real decision is to identify which path suits which workload, from integrations and automation to API and agent management. Here’s what you need to consider:

Integration

Every connection you add also means a decision needs to be made about who will maintain it for the next five years.

• Build: You can write custom code per connection in-house, with full control of the logic and the intellectual property remaining with you. Initial development typically accounts for a fraction of an integration’s lifetime cost, but every new system is another point-to-point project, and custom integrations turn into black boxes once the specialist talent moves on.
• Buy: Pre-built connectors and low-code tooling, real-time pipelines, change data capture, and endpoint maintenance can be supplied and handled by your vendor. It’s the fastest route to production, with your reach bounded only by what the connector catalog covers.
• Use AI: Describe what you want in plain language and a working connector appears in minutes, open to people who don’t write code. But output is non-deterministic and needs review, and unless the artifact lands as a governed component, there’s no reuse: fresh code every time, and fresh code is extra maintenance for somebody.

Automation

Automation supplies the necessary scheduling, retries, error handling, and compensation logic.

• Build: In-house hand-coded orchestration for every process offers total flexibility, but it comes with the knowledge that every edge case is yours forever.
• Buy: When workflow automation with event triggers, advanced error handling, role-based access control, and activity monitoring is already in the platform, those behaviors stop being things each process must implement. Low-code tooling opens the work to your business users, useful because most data inconsistencies are business problems rather than technical ones.
• Use AI: Agents can orchestrate and adapt workflows at runtime instead of following a fixed graph. That suits an exception-heavy claims process, but fits badly with a nightly reconciliation that has to run identically every time. You need guardrails and approval gates to control which of the two you get.

API Management

Your API estate is crucial because it’s what agents will reach for first.

• Build: In-house, you’ll assemble a gateway, developer portal, security layer, versioning, and monitoring from separate parts, then maintain them across the whole API lifecycle, retirement included.
• Buy: A platform can deliver full lifecycle management from one control plane, with federated governance across the gateways you already run. Most enterprises didn’t choose to have multiple gateways; they grew through acquisition, or business units picking different vendors. Federation gives you centralized policy with decentralized ownership, so teams keep their own APIs while organization-wide rules for authentication, quotas, and compliance still apply.
• Use AI: Agents consume your APIs as tools, increasingly through the Model Context Protocol (MCP), which turns every undocumented endpoint into an agent-reachable attack surface.

AI Agent Management

While agent creation now feels frictionless, supplying much-needed governance is often an afterthought.

• Build: You’ll need your own registry, guardrails, telemetry, and audit trails. This gives you full control, but it enters you in a race you’ll probably lose, because no-code builders let one team stand up hundreds of agents while your governance layer lags far behind.
• Buy: A vendor can offer a central registry, real-time monitoring, anomaly detection, and guardrails native to the platform, covering agents built elsewhere as well as your own. And, since nobody’s agent estate comes from a single vendor, look for cross-provider coverage to separate the tools that deliver real oversight from those that give only a partial view.
• Use AI: This path doesn’t offer an alternative to agent management, it creates the requirement you need to solve in the first place. Every agent that builds something produces more for you to govern.

Can AI Run Your Integrations?

People often think, “The demo works, so why buy a platform?” The answer is that AI agents are only one layer of an enterprise platform architecture. On their own, agents can automate mapping, generate workflows, and predict failures, but you still need a platform to supply the runtime, the connectors, the monitoring, and the compliance controls.

Let’s examine what managing integrations with AI looks like.

When AI Agents Can Run Integrations

A small, technically capable team can use AI to build selected integrations or its own MCP servers without much trouble. If your entire requirement is one stable internal API you control, the maintenance burden stays low, and buying a platform to solve it would be overkill.

But pilot execution and enterprise-wide deployments are different problems, and the transition between them is where teams get stung.

When AI Agents Require Platform-Level Governance

At enterprise scale, AI Agents can’t be trusted to govern themselves, and the manual governance burden is unsustainable. Agentic governance platforms help enterprises control for six key risks of agentic sprawl:

1. Unbounded execution: Agents can misinterpret instructions and trigger a chain of actions faster than users, IT, or security can intervene.
2. Over-privileged tools: Agents handed broad credentials or long-lived static API keys hold credentials and privileges beyond the scope that their tasks require.
3. Poor auditability: When something goes wrong, you have to answer which agent did what, on whose behalf, and when, and most teams can’t.
4. Data quality and lineage failures: Data analysis by agents is only as good as the lineage behind it. Agents working on stale or fragmented data produce unreliable results with total confidence, and cleaning up after automated mistakes costs far more than stopping them in the first place.
5. No lifecycle control: Agents are still production software and need versioning, rollback, retirement, and cost controls like any other system.
6. Shadow AI: Agents and integrations created outside your IT and security teams’ field of view increase your attack surface. In Omnia’s research, 81% of senior decision-makers stated that unmanaged shadow integrations were already damaging data quality and confidence.

The Maintenance Burden of Using Agents to Govern Agents

In a pilot, the main question is whether you can get an AI to call an API, and the answer is usually yes, easily. But in enterprise deployments, the questions change completely:

• Who authorizes each agent action?
• How is least privilege actually enforced?
• How are failures, retries, and rollbacks handled?
• How do you prevent prompt injection and unsafe tool use?
• How do you audit thousands of agent actions?
• Who owns this at 3 a.m.?

None of these are integration code questions; they’re operations and governance challenges, and each hand-built MCP server you add needs credential storage, policy logic, and monitoring processes. Multiply that within the enterprise, and your records end up scattered across a dozen incompatible places with no audit trail or policy enforcement tools. Governance machinery, rather than integration code, becomes the product you spend most of your time on.

The constructive solution isn’t to abandon AI, but to give it a governed control plane to act on. By exposing APIs you already have through a managed MCP layer, authentication, policy enforcement, monitoring, and audit trails are applied everywhere instead of reimplementing them inside every server. The same agents run at the same speed, but with one place where authorization is enforced and recorded. That agentic AI data foundation is what a platform brings to the table.

Forrester’s Agentic AI Readiness Gap Report shows that only 34% of leaders currently trust their agentic systems — and sets you on the path to trusted agentic systems.

The Alternative to Build vs. Buy Integration Platforms: Partnering

With partnering, you can enjoy the benefits of all three paths: the platform supplies a governed foundation, experts ensure that functionality and integrations are tailored to your market and business goals, and AI accelerates work and provides intelligent automation with a single point of observability. Your partner works alongside your team, bringing accountability and cross-industry expertise under your strategic guidance.

Here’s how it works:

• Integration: Application connectors handle the most common scenarios, and partner expertise provides the customizations that suit your needs, so there’s no point-to-point sprawl to maintain.
• Automation: Pre-built recipes accelerate your workflow development time, with error handling, access control, and activity monitoring baked into the platform.
• API management: Full lifecycle, federated governance is available immediately, with no stack to assemble and nothing to keep patched.
• AI agent management: The Agent Control Plane establishes a registry, guardrails, and observability before your agents scale, rather than retrofitting governance on to agent sprawl.
• Reuse: Assets built in the platform are composable, so what you deliver this quarter can be reused and adapted for other departments and use cases.
• Trusted Foundation: SLAs, security posture, compliance certifications, and platform upgrades are maintained by your vendor and partner instead of absorbed by a team that already has other work.

Deloitte’s Tech Trends 2026 research found AI agent pilots built through strategic partnerships twice as likely to reach full deployment as pilots developed internally. But a tool that ships and nobody uses is still a failed project. So, it’s reassuring to know that the same research found that employee usage of externally built agentic tools runs at nearly double the rate of in-house ones.

It should be no surprise, then, that 85% of high-performing AI adopters collaborate frequently with strategic partners, and 90% of technology executives plan to expand their ecosystems and partnerships specifically to secure the expertise needed to scale agentic systems.

How to Choose: Matching the Option to Your Enterprise

When deciding between buying an enterprise platform, managing integrations with AI Agents, or keeping your current environment, there are many factors to consider beyond recurring expense:

Talent and Maintenance Workload

Building your own tools results in work tailored to your business, but can have significant downstream costs: development takes longer and diverts revenue-generating resources, and long-term maintenance will require internal documentation and training for new team members tasked with upkeep.

Buying is faster, and established work patterns may need to adapt to the platform during implementation and adoption, which can create temporary slow-downs. However, the operational and maintenance work is handled by the platform, and a platform with a robust training ecosystem means you don’t have to rely on internally-created training and documentation with every new hire.

Integration and Automation Landscape

Once business processes span three or four systems, there is value in an approach that scales. Reusable components means that new connections and workflows won’t disrupt existing architecture or create manual configuration work.

Your API estate also deserves its own assessment. How many APIs do you have, how many gateways, and who governs them? API lifecycle management, auto-discovery, and federated management streamline your API management work streams and simplify policy enforcement.

Composability and Reusable Components

How frequently is your IT team asked to implement new integrations, automations, or to add new agentic capabilities? Each feature request shipped without the governance, documentation, and reusability offered by an enterprise platform increases long-term technical debt.

Even though most organizations know this, they act otherwise. In the IDC InfoBrief for IFS and Boomi, 89% of respondents identify API-driven innovation as a key component of composability, and reusability of digital assets appears among the eight named drivers of composable adoption. Yet 41% had no composability strategy at all. With no framework for reuse, their teams are burdened by technical debt.

Cost Control

Each path balances its costs in different areas:

• Building is often seen as a sunk cost: using existing salaried staff resources to create internal tools. This sunk cost view can often downplay the costs of ongoing updates and bug fixes.
• Buying breaks down headcount or consumption counts into a subscription, making platform expenses simple to manage, rolling upkeep into predictable costs.
• Using AI is facing the cost problem of unpredictable token consumption. Although per-token prices have fallen by more than 90% since 2023, the total spend on large language models has roughly doubled in under a year. Uber burned through its entire 2026 AI budget in four months and now caps AI spend at $1,500 per employee each month. The problem is growing ever worse thanks to agentic workloads.

Unsure about your token spend strategy? You’re not alone. Learn how to manage spend and get a clear picture of your AI ROI with our Playbook for Controlling Costs in the Agentic Enterprise.

What Your Agentic AI Data Foundation Has to Supply

An agent can’t report spending without cost and revenue data, can’t build integrations between software it doesn’t have access to, can’t reason over data your systems never reconciled, and can’t outlive a model your contract locked you into. None of those are the fault of your agent. To avoid these limitations, your agentic AI data foundation has to meet four requirements:

1. Governance: Secure, Reliable, Centralized

Governance is what most organizations name as their biggest constraint, with 33% citing data security, privacy, and risk as a significant factor shaping AI strategy over the next six months.

Two things make governance straightforward with the centralized management offered by a platform:

• APIs: policy enforcement, quotas, security scoring, and discovery of shadow and zombie APIs across every gateway you run.
• Agents: real-time telemetry, anomaly detection, and audit logs, which together let you answer what your agents are reaching into, how much they’re spending, and what errors they’re making.
• Integration: error handling, trace logging, and performance management. Visibility into errors with integrations before they become long-term performance gaps.
• Data: data masking to protect confidential information, data residency controls, hybrid runtime capability, data lineage.

2. Scalability: Reuse, Maintenance, Runtime Deployment

A prototype that has to be rebuilt before it can run in production isn’t a prototype; it’s just a demo. With reusable, governed components, you can cut both duplicated build effort and repeat maintenance. Look for flexible runtime deployment options so your agents can move into production without being re-engineered for the target environment.

3. Data Access With Business Context

Agentic work draws on more of your software and technology stack than previous automation tools. A single request reads records, crosses systems, triggers a process, and acts on the result, so any stage that isn’t working properly stops the whole thing or corrupts it. These are the five capabilities that have to be in place:

1. Data readiness: Agentic AI for data quality depends on validated, deduplicated data, with master data management producing trusted golden records and enforcing schema validation, type rules, and checks for missing or invalid fields. Organizations succeeding in AI invest up to quadruple the amount in their data and analytics compared to those who fail.
2. Business context: Semantic metadata and endorsed glossaries grounding how your agents reason.
3. Connectivity: Fragmented on-premises and cloud sources unified into real-time workflows across structured, semi-structured, and unstructured formats.
4. Automation: Event-driven triggers, cross-system orchestration, retries, and error handling as platform behavior rather than something written into each process.
5. Agent Control: Governed from one control plane instead of a scattering of disconnected tools.

Data silos and fragmentation are the single biggest obstacle to using agentic AI for data quality at scale. Nearly half of organizations name data searchability (48%) and reusability (47%) as primary obstacles to their AI automation strategies, and 36% report that scaling AI across teams is a major struggle.

4. Vendor Independence and Future-Proofing

Models and agent frameworks will keep changing, so a functional, governed agentic AI data foundation needs to change with them. Your foundation should be model-agnostic and vendor-agnostic, letting you run whatever model suits the task, swap it when a better one appears, and stay out of anyone’s walled garden. Portability is also becoming a procurement requirement, with 77% of surveyed companies including an AI solution’s country of origin in vendor selection criteria.

Choose Boomi to Activate Agentic AI Data and Integration

Agents need a robust platform, whichever path you take. Boomi supplies the agentic AI data foundation of consistency, dependability, and safety to more than 30,000 customers and a network of 800+ partners in one human-engineered platform, not a stack you assemble yourself.

Here’s what you get with Boomi:

Data and Integration

These are just some of the reasons why Boomi is the platform of choice for integration and automation:

• Boomi Data Integration: real-time pipelines driven by change data capture (CDC), unifying data workflows across cloud and on-premises sources.
• Boomi Data Hub: master data, conflict resolution, and cross-system synchronization, producing a trusted single source of truth to ensure agentic AI for data quality.
• Boomi Meta Hub: endorsed business glossaries and semantic metadata, with each definition tied to the schemas, connectors, and agents it governs, grounding your agents in proprietary business context.

Scalability

• Reuse integration templates, apps, APIs, and agents, so you duplicate less and keep experiences consistent across systems.
• Flexible runtime across on-premises, cloud, and edge, sized for hybrid architectures.
• Integration runtimes and API gateways deployable across AWS, Azure, and Google Cloud without lock-in.

Automation

• Boomi Flow: low-code workflow automation with event triggers, built by your business and technical teams together.
• Emtec cut order entry time by 85% on its order-to-cash process using Boomi’s pre-built SAP and Salesforce connectors, which made implementation faster and cheaper than a custom build.

API Management

Boomi has also achieved the rank of Leader in the 2025 Gartner Magic Quadrant for API Management, thanks to features like:

• Federated API management across Boomi, cloud, and third-party gateways, giving you one trusted API inventory.
• Shadow and zombie API discovery, policy-driven security, and MCP-enabled APIs exposed as governed agent tools.

AI Agent Management

More than 90,000 platform agents are deployed in production today, supported by tools like:

• Boomi Agentstudio: design, govern, and orchestrate all your AI agents at scale, whether they were built on Boomi or somewhere else.
• Agent Control Tower: a central registry, real-time monitoring, anomaly detection, a kill switch for compromised agents, and per-invocation token telemetry that turns AI spend from a surprise into a managed line item.

Vendor Independence and Future-Proofing

• Cross-provider governance covering Amazon Bedrock, Salesforce Agentforce, Microsoft Copilot, and Snowflake Cortex agents from one place.
• Models swappable at any time, with nothing proprietary you can’t leave.

Better Together: Boomi Companion

• Boomi Companion is an open-source suite of Agent Skills that gives third-party AI agents deep knowledge of the Boomi platform, so natural-language requests become real, secure work on your Boomi account.
• It’s agent-agnostic, running on Claude Code, Claude Cowork, OpenAI Codex, GitHub Copilot, Google Antigravity, and 40+ other AI tools.
• Describe, build, deploy, pull logs, diagnose, modify, and redeploy inside one conversation, so what comes back for your review is a working integration rather than a half-built one.
• Companion configures real Boomi components against your infrastructure instead of producing the brittle one-off code that makes the “use AI” path fail everywhere else. Your agent’s output becomes governed, reusable componentry, with Boomi conventions applied from the first prompt. Boomi reports up to a 16x increase in development speed. Companion is free with any Boomi platform license.

Want to run your integrations, APIs, and agents on one governed platform, engineered to be predictable, reliable, and secure? Get the evaluation phase right to build a long-term partnership. Read the 12 Essential Reasons to Choose the Boomi Platform.