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November 21, 2025
AI agents introduce a new world of opportunity for teams who build enterprise automations to improve and accelerate processes. Suddenly, LLM-backed agents make workflows possible that were previously too unpredictable or dependent on human decisioning.
Enterprise leadership has taken notice.
As IT teams face pressure to deliver AI value for the enterprise, you’re hyper aware that the inconsistent and inaccurate outcomes that large language model (LLM) applications introduce just won’t cut it in business-critical applications. Accuracy and reliability is what you’ve spent your integration career solving for.
We know this very well. Agents are the future, and we want onboarding teams to be responsible for them. At Digibee, we spend a lot of time thinking about the challenges agents face and how to overcome them.
What we can't change (yet)
As of this post (AI changes fast!), LLMs suffer from three broad and unavoidable challenges that reduce agent reliability:
- Hallucinations: LLMs consistently generate outputs that sound right, but they’re sometimes incorrect.
- Non-determinism: Even with identical input prompts, LLMs outputs can vary.
- Context limitations: The more tokens an LLM takes in, the more likely it is to make a mistake.
To get accurate trustworthy agents to production despite these challenges, developers must build robust systems around them.
Three approaches, and where agents fit
Automations help people accomplish tasks while minimizing or eliminating human involvement. As exciting new approaches emerge, it’s important to anchor to the right solution for the task.
Broadly, automation approaches fall into three categories:
- Deterministic automation: Specific triggers induce specific actions, such as a user submitting a password reset request and receiving a link by email.
Best for use cases with highly predictable inputs and processes.
- Autonomous agents: Agents dynamically coordinate themselves, deciding when to collaborate, delegate, and hand off tasks.
Best for use cases that are highly open-ended and actively keep humans in the loop.
- Orchestrated agents: Humans define the sequence and structure of agent inputs and actions with minimal agentic self-direction.
Best for use cases that benefit from agent creativity at key steps but require overall accuracy and predictability.
Industry data overwhelmingly supports orchestrated agents for production deployments. This approach costs 4-15x less than autonomous agents while delivering better reliability.
Organizations like Mayo Clinic, Kaiser Permanente, ServiceNow, and PwC universally use orchestrated agent patterns in production deployments for reliability, cost control, and compliance. Studies analyzing multi-agent systems found that 60% of attempts to deploy autonomous agents fail to scale beyond pilots, primarily due to coordination complexity and specification failures.
The strategies below focus on orchestrated agents—the proven approach for enterprise deployments where accuracy and predictability matter.
Legacy APIs weren’t built for agents
Enterprise AI agents derive impact from their interaction with APIs. But API architectures (particularly for legacy systems) can hamper agent success.
Agents get lost with “chatty” APIs
Each step an agent takes balloons the likelihood of a failed transaction, inhibiting their reliability in extended transactions.
- Problem: "Chatty" REST APIs often require multiple sequential calls to achieve a single outcome (e.g., fetching an employee, then their team ID, then their manager).
- Solution: Instead of exposing individual API calls, build a single tool that encapsulates all the necessary underlying API interactions—for example "getEmployeeManager(employeeName)." The agent calls one tool which handles the internal orchestration. This minimizes agent steps and improves accuracy.
Legacy APIs under-explain errors
Agent pipelines can “self-heal" when they encounter API errors. They can call endpoints at increasing delays or update payload structures to match a changed field name. However, this requires sufficient information.
- Problem: Traditional APIs built for software consumption often return generic error codes or empty responses.
- Solution: MCP pipelines (see callout box) can enrich API error responses to translate "404 Not Found" into a semantically rich message like, "Error: Employee ID does not exist in the system. Please verify the ID." This detailed feedback empowers the agent to understand the error, attempt a corrective action, or request clarification from the user.
Digibee MCP Pipelines
In Digibee, pipelines are how integration teams have always orchestrated complex workflows across systems and data.
When an agent should do something the same way every time (no creativity or autonomy is valuable) a pipeline becomes the perfect way to do so.
Digibee natively delivers these pipelines as MCP tools for any agent to use.
AI agents can struggle with in-prompt rules
For agents to generate business value, they must follow business rules. Unbound agents can take actions that hurt the business, like selling a pickup truck for $1 (though the consequences are usually more subtle).
Agents wander from company playbooks
In many use cases, an agent must follow a specific order of operations—for example, checking a customer's credit score before approving a loan.
- Problem: Studies have found that agents can veer from even the most clearly described process orders, making their “creativity” into a liability.
- Solution: Orchestrate agents in a defined, deterministic manner. By integrating agents into a structured pipeline, you enforce the business playbook, use agent creativity only when helpful, and ensure each step is executed in the correct order.
Nuanced business rules get lost
Nuanced business rules (such as airline baggage allowances by ticket class) often confuse human customers and employees. AI agents, trained on human writing, share this limitation at 1000x the scale.
- Problem: LLM’s non-deterministic nature can cause them to act inconsistently when following prompt-based rules—creating auditability and compliance issues.
- Solution: Instead of embedding business rules into agent prompts, turn them into deterministic MCP pipelines This change forces the agent to execute rules with 100% predictability. This ensures compliance, provides an auditable logic trail for every decision, and removes the risk of LLM misinterpretation.
Cluttered contexts confuse agents
Behind the scenes, each action an agent takes starts with a prompt to an LLM. The size and structure of the prompt can significantly impact whether or not the action succeeds.
Vital information can get lost in long contexts
Every piece of information sent to or generated by an agent lengthens its “context,” which includes system prompts, user messages, tool descriptions, and the text within each step of an agent's reasoning.
- Problem: High token counts increase the agent’s cost and degrade its accuracy; vital information can get “lost in the middle.” In extreme cases, token counts can exceed the LLM’s “context window” and trigger errors.
- Solution: MCP pipeline tools can use patterns familiar to integration platform as a service (iPaaS) customers to narrowly expose information. Instead of an entire customer record, these intelligent wrappers use existing APIs to return only the fields necessary for each transaction, reducing token consumption and increasing accuracy.
Token consumption examples
System prompt: The initial instructions that set an agent’s behavior, tone, and/or role prior to any user interaction.
User messages: The text inputs or queries human users provide when interacting with the AI system.
Tool descriptions: Brief explanations of external tools or functions an AI agent can call (e.g., a web search, calculator, or API).
Stages: Ordered actions or stages an AI workflow or process follows to complete a task.
Agents fumble with too many tools
The choice and design of tools available to an agent significantly impacts its performance. Researchers at Microsoft recently identified 1.470 unique MCP servers across smithery.ai and Docker MCP Hub and catalogued a host of problems with the available selection—including hundreds of tool "collisions" likely to confuse models.
- Problem: Too many tools, tools with overlapping functionalities, or poorly described tools make it harder for the agent to select the correct one for a given task, reducing accuracy.
- Solution: Digibee's platform allows users to create purpose-built MCP tools highly focused on specific tasks. It also allows users to dynamically filter the tools presented to an agent. This reduces ambiguity and improves the likelihood of correct tool utilization.
How insufficient observability hinders agent impact and iteration
Building agents is an iterative process. To continuously improve their accuracy and performance, robust observability and evaluation mechanisms are indispensable during both build and production—but often lacking.
- Problem: Without insight into an agent's internal workings, developers struggle to identify patterns of failure or inefficiency.
- Solution: With Digibee, users can analyze and evaluate saved agent traces , gaining a transparent view into every step, tool call, and response. Users can then spot repetitive patterns where agents struggle or take suboptimal paths.
This analytical capability is critical for implementing changes that lead to more accurate and efficient agents, effectively making the development and improvement process data-driven.
You can build effective enterprise agents
Building accurate and reliable AI agents is complex. It requires careful consideration of LLM behaviors and the right solutions to overcome accuracy challenges.
With Digibee, integration teams can get trustworthy agents to production faster by:
- Leveraging orchestrated agent workflows to limit unwanted agent creativity.
- Facilitating MCP tools that enforce deterministic rule execution.
- Optimizing token usage through data transformation.
- Consolidating “chatty” APIs to reduce agent actions.
- Enabling intelligent error handling to create “self-healing” agents.
- Delivering comprehensive evaluation capabilities.
