Tools: AI Agents Explained: How They Automate Enterprise Workflows

What An Agent is Made Of ## Single Agent in Action: Document Processing ## Multiple agents: lead-to-cash automation ## What Breaks and How to Prevent It ## Getting started without Over-Engineering Last year I spent a few months helping a team wire up an AI agent to their claims processing pipeline. The first version was embarrassingly simple. It was a loop that read a claim, called three APIs, and decided what to do next based on the responses. It looked nothing like the diagrams in the "agentic AI" blog posts I'd been reading. But it worked. And the reason it worked is that AI agents aren't actually that exotic if you've spent time building with APIs. You already know how to call a function, read the response, and branch based on what comes back. An agent does the same thing. The difference is that the branching logic is handled by an LLM instead of your if/else statements. The model reads the result, decides what to call next, and keeps going until the job is done or it runs out of steps. That's the whole concept. Everything below is about what that looks like when you're building it for real: the loop, the moving parts, and the things that go wrong when you move from a prototype to an actual enterprise workflow. The Loop A chatbot takes an input and returns a response. One shot. An agent takes a goal and works toward it over multiple steps. Here's the pattern, called ReAct (Reason + Act): Goal: "Process this refund request" Thought: I need the customer record. Let me look it up by ID. Action: customer_lookup(id="12345") Result: { error: "not found" } Thought: ID didn't work. The original request has an email. I'll try that. Action: customer_lookup(email="[email protected]") Result: { id: "67890", name: "Jane Smith", status: "active" } Thought: Got the record. Now I can process the refund. Action: issue_refund(customer_id="67890", amount=49.99, reason="defective item") Result: { success: true, transaction_id: "TXN-882" } Notice what happened on the second step. The lookup failed, and the agent didn't crash or throw a 500. It looked at the error, decided to try the email field instead, and continued. That's the whole point. RPA follows a fixed script. If step 2 breaks, the process breaks. An agent reasons through the failure and tries something else. This loop (think, act, observe, adjust) repeats until the goal is met or a step limit stops it. Everything else in this article is built on top of that loop. Four pieces. Every framework organizes these differently, but they're always there. Persona. The system prompt. It tells the agent what it is, what it can do, and what it must never do. Think of it as a job description combined with a policy manual: You are a refund processing agent. You may look up customer records and issue refunds up to $500. You must NEVER delete customer accounts. You must ALWAYS confirm the refund amount before executing. If you skip this, the agent will improvise. If it has access to a delete_account tool and no rule against using it, it can and eventually will call it. The persona is where you set the guardrails. Memory. Two kinds. Short-term memory is just the conversation context: what's happened so far in this session, held in the LLM's context window. Long-term memory is external: a vector database, a knowledge graph, a regular Postgres table. When the agent needs information beyond the current session (customer history, compliance rules, product specs), it queries long-term memory. This is how RAG works in practice: the agent pulls relevant context from your data before it reasons. Planning. The reasoning engine. This is where the ReAct loop lives. The agent breaks a goal into steps, executes them, and adjusts as it goes. More advanced patterns exist (Plan-and-Solve generates the full plan upfront; Tree-of-Thought explores multiple paths before committing), but ReAct handles most enterprise use cases fine. Tools. Functions the agent can call, defined with typed schemas: Tool: issue_refund Description: Issues a refund to a customer's original payment method. Parameters: Here's something most people learn the hard way: the quality of your tool schemas matters more than which model you use. The agent picks tools based on their descriptions. Vague description → wrong tool selected. Missing parameter constraint → runtime error. Teams that build reliable agents spend most of their development time on schema definitions, and it shows. A compliance team gets hundreds of regulatory filings per week. Each one needs to be classified, checked against policies, and routed to the right reviewer. Four steps. The agent handled classification, policy checking, and routing. Those were tasks that used to involve three different people and a shared spreadsheet. And when a filing comes in with a format the agent hasn't seen before, it doesn't silently misclassify. It flags the uncertainty and escalates. This is the same pattern behind a pharmaceutical audit analytics system that Ciklum built to process over 400,000 audit findings. Manual categorization had been error-prone, and those errors were undermining leadership decisions. The ML pipeline replaced it with automated, context-driven tagging. Every tag was traceable back to the original data. Enterprise processes almost never fit inside one agent. A lead-to-cash workflow spans demand generation, quoting, order fulfillment, and invoicing. Different data sources, different rules, different teams. Multi-agent systems handle this the way microservices handle a monolith: by splitting responsibilities. Orchestrator ├── Demand Agent → qualifies leads, scores opportunities ├── Quote Agent → generates pricing, checks inventory ├── Fulfillment Agent → triggers provisioning, tracks delivery └── Invoice Agent → generates invoices, monitors payment The orchestrator holds the workflow state and decides which agent runs next. Each sub-agent handles its own domain, calls its own tools, and reports back. When the Quote Agent can't find pricing for a custom configuration, it doesn't guess. It escalates to the orchestrator, which routes the exception to a human. I have seen Ciklum, a leading AI-powered Experience Engineering firm, help a cloud computing company redesign its entire lead-to-cash pipeline using this pattern. The system combined 40+ automation bots, intelligent document processing, and process mining into a coordinated pipeline. The company serves 100,000+ enterprise customers. At that scale, a single-agent approach wouldn't hold. Here's where the gap between demo and production shows up. Agents call tools that don't exist. Or they pass the wrong argument types. This happens more often when tool descriptions are vague. Fix: validate every tool call against the schema before executing it. Feed validation errors back to the agent so it can self-correct. Agents get stuck in loops. A ReAct agent that gets a confusing observation can retry the same action endlessly, or bounce between two actions without progress. Fix: set a max_steps limit. 10–15 steps works for most workflows. If the agent hits the ceiling, it escalates to a human instead of spinning. Agents trust bad input. Indirect prompt injection is a real risk in enterprise settings. A malicious instruction hidden in a document (for example, white text on white background or a comment in a PDF) can redirect the agent's behavior. Fix: treat all external content as untrusted. Scan it with a separate model or classifier before passing it to the agent. Context windows overflow. Long-running workflows accumulate token history that degrades the model's attention and inflates cost. Fix: prune the context. After each major step, summarize what's done and drop the raw history. The agent works from the summary plus the current step. Nobody can explain what happened. When an agent makes a bad decision, standard application logs (200 OK, 43ms response time) tell you nothing about why. You need the full reasoning trace: the system prompt, the input, each thought-action-observation cycle, and the final output. Without this, debugging agent behavior is guesswork. Connecting AI to your business systems through standardized protocols like MCP helps here because logging becomes consistent across every data source instead of needing per-connector instrumentation. If you're building your first agent, start small. Pick one workflow that currently involves a human doing the same sequence of steps repeatedly. Map out the tools that workflow needs (probably 3–5 API calls). Write the tool schemas with obsessive detail. Set a strict persona. Set a step limit. Wire up the ReAct loop and see what happens. Don't start with a multi-agent system. Don't start with a complex orchestration layer. Get one agent working reliably on one workflow, understand where it fails, and expand from there. The engineering discipline is familiar even if the technology feels new. Define clear interfaces. Handle errors by feeding them back into the system. Set boundaries. Validate outputs. The same instincts that make you a good API developer make you a good agent developer. The twist is that you have a probabilistic reasoning engine in the middle of your control flow now, so you have to plan for the cases where it's confidently wrong. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - customer_id: string (required) - amount: number (required, max 500) - reason: string (required) Returns: { success: boolean, transaction_id: string } - agent extracts text, classifies document type - agent checks text against compliance ruleset - result: non-compliant, missing disclosure section - agent routes to senior reviewer with findings attached, priority high