AI Agent Enterprise Use Cases: How Big Companies Use AI

AI Agent Enterprise Use Cases: How Big Companies Use AI

💡 What You Will Learn

AI Agent Enterprise Use Cases: How Big Companies Use AI

# Intelligent/SmartAgent Architecture
class CustomerServiceAgent:
    def __init__(self):
        self.intent_classifier = load_model("intent-classifier-v2")
        self.knowledge_base = VectorStore("product_kb")
        self.order_api = OrderAPIClient()
        self.escalation_threshold = 0.7  # 70%

    def handle_request(self, user_message, user_id):
        # 1. 
        intent = self.intent_classifier.predict(user_message)
        # 2. 
        sentiment = analyze_sentiment(user_message)
        if sentiment == "angry" and intent == "complaint":
            return {"action": "escalate", "reason": ""}

        # 3. 
        handlers = {
            "order_status": self.query_order,
            "return_request": self.process_return,
            "product_inquiry": self.answer_product,
        }
        handler = handlers.get(intent, self.fallback_handler)
        result = handler(user_message, user_id)

        # 4. 
        if result.confidence < self.escalation_threshold:
            return {"action": "escalate", "reason": f"{result.confidence}"}

        return {"action": "reply", "content": result.answer}

|:----|:----------|:------------|:----| || 65% | 82% | +17% |

# Agent
def enterprise_knowledge_agent(query, employee_role):
    """"""
    # 
    accessible_docs = get_docs_by_role(employee_role)
    # RAGRetrieve
    relevant_chunks = vector_db.search(query, accessible_docs, top_k=3)
    # 
    prompt = f"\n{relevant_chunks}\n{query}"
    answer = llm.generate(prompt)
    return answer

# 500
# |  |  | AI |
# |:--------|:----|:--------|
# | HR// | 35% | 94% |
# | IT/Installation/Setup | 28% | 88% |
# | / | 22% | 91% |
# |  | 15% | 75% |

# AI Code Review Agent
class CodeReviewAgent:
    def __init__(self, repo_rules):
        self.rules = repo_rules  # 

    async def review_pr(self, pr_number, changed_files):
        issues = []
        for file_path, diff in changed_files.items():
            # 
            violations = self.check_style(diff, self.rules.style_guide)
            # Testing
            test_coverage = self.analyze_test_coverage(file_path, diff)
            # Security
            security_issues = self.scan_security(diff)
            # 
            improvements = self.suggest_refactoring(diff)

            issues.extend(violations + security_issues + improvements)

        return {"pr": pr_number, "issues": issues, "review_summary": self.summarize(issues)}

# 
# |  | Review | AI +  |  |
# |:----|:-----------|:---------|:----|
# | PR | 45 | 8 | 82% |
# | Bug | 72% | 89% | +17% |
# | Security | 12% | 2% | -10% |

# Agent
class SalesAssistantAgent:
    def analyze_lead(self, lead_info):
        """→→"""
        profile = self.build_customer_profile(lead_info)
        needs = self.predict_needs(profile)
        recommendations = self.gen_product_recommendations(needs)
        email_draft = self.write_followup_email(recommendations)

        return {
            "profile": profile,
            "needs_score": needs['score'],  # Need intensity 0-100
            "top_products": recommendations[:3],
            "email_draft": email_draft
        }

|:----|:----|:----|:-------|