Use Cases and Applications

1class CodingAgent:
2    """Agent specialized for code-related tasks."""
3
4    def fix_bug(self, error_description: str) -> str:
5        # 1. Search for relevant files
6        files = self.tools.search_codebase(error_description)
7
8        # 2. Read and understand the code
9        context = self.tools.read_files(files)
10
11        # 3. Identify the issue
12        analysis = self.llm.analyze(
13            f"Given this error: {error_description}\n"
14            f"And this code: {context}\n"
15            f"What is the root cause?"
16        )
17
18        # 4. Generate a fix
19        fix = self.llm.generate(
20            f"Generate a fix for: {analysis}"
21        )
22
23        # 5. Apply and test
24        self.tools.edit_file(fix)
25        test_result = self.tools.run_tests()
26
27        # 6. Iterate if needed
28        if not test_result.passed:
29            return self.fix_bug(test_result.errors)
30
31        return f"Fixed: {analysis}"

1class ResearchAgent:
2    """Agent for conducting research tasks."""
3
4    def research_topic(self, topic: str) -> str:
5        # 1. Generate search queries
6        queries = self.llm.generate_queries(topic)
7
8        # 2. Search multiple sources
9        results = []
10        for query in queries:
11            results.extend(self.tools.web_search(query))
12            results.extend(self.tools.academic_search(query))
13
14        # 3. Fetch and read content
15        documents = []
16        for result in results[:20]:  # Top 20 results
17            content = self.tools.fetch_page(result.url)
18            documents.append({
19                "source": result.url,
20                "content": content,
21            })
22
23        # 4. Extract key findings
24        findings = self.llm.extract_insights(documents, topic)
25
26        # 5. Synthesize into report
27        report = self.llm.synthesize_report(
28            topic=topic,
29            findings=findings,
30            sources=documents,
31        )
32
33        return report

1class DataAgent:
2    """Agent for data analysis tasks."""
3
4    def answer_question(self, question: str) -> dict:
5        # 1. Understand the database schema
6        schema = self.tools.get_database_schema()
7
8        # 2. Generate SQL query
9        sql = self.llm.generate_sql(
10            question=question,
11            schema=schema,
12        )
13
14        # 3. Validate the query (safety check)
15        if not self.validate_sql(sql):
16            return {"error": "Unsafe query generated"}
17
18        # 4. Execute the query
19        results = self.tools.execute_sql(sql)
20
21        # 5. Analyze the results
22        analysis = self.llm.analyze_results(
23            question=question,
24            data=results,
25        )
26
27        # 6. Create visualization if appropriate
28        if self.should_visualize(results):
29            chart = self.tools.create_chart(results)
30            analysis["chart"] = chart
31
32        return {
33            "answer": analysis,
34            "sql": sql,
35            "data": results,
36        }

1class DevOpsAgent:
2    """Agent for DevOps automation tasks."""
3
4    def handle_alert(self, alert: dict) -> str:
5        # 1. Analyze the alert
6        analysis = self.llm.analyze_alert(alert)
7
8        # 2. Gather diagnostic information
9        logs = self.tools.fetch_logs(
10            service=alert["service"],
11            timeframe="last_hour",
12        )
13        metrics = self.tools.fetch_metrics(
14            service=alert["service"],
15        )
16
17        # 3. Identify root cause
18        diagnosis = self.llm.diagnose(
19            alert=alert,
20            logs=logs,
21            metrics=metrics,
22        )
23
24        # 4. Determine remediation
25        if diagnosis.severity == "low":
26            # Auto-remediate
27            fix = self.generate_fix(diagnosis)
28            self.tools.apply_fix(fix)
29            return f"Auto-remediated: {diagnosis.summary}"
30        else:
31            # Escalate to human
32            self.tools.notify_oncall(
33                alert=alert,
34                diagnosis=diagnosis,
35            )
36            return f"Escalated: {diagnosis.summary}"

1class SupportAgent:
2    """Agent for customer support tasks."""
3
4    def handle_ticket(self, ticket: dict) -> str:
5        # 1. Understand the issue
6        issue = self.llm.classify_issue(ticket["message"])
7
8        # 2. Search knowledge base
9        articles = self.tools.search_knowledge_base(issue)
10
11        # 3. Check customer history
12        history = self.tools.get_customer_history(ticket["customer_id"])
13
14        # 4. Generate response
15        response = self.llm.generate_response(
16            issue=issue,
17            articles=articles,
18            history=history,
19        )
20
21        # 5. Determine if needs escalation
22        if issue.confidence < 0.8 or issue.severity == "high":
23            self.tools.escalate_to_human(ticket, response)
24            return "Escalated with draft response"
25
26        # 6. Take action if needed
27        if issue.requires_action:
28            self.tools.execute_action(issue.action)
29
30        return response

1class ContentAgent:
2    """Agent for content creation tasks."""
3
4    def create_blog_post(self, brief: dict) -> dict:
5        # 1. Research the topic
6        research = self.research_agent.research_topic(brief["topic"])
7
8        # 2. Create outline
9        outline = self.llm.create_outline(
10            topic=brief["topic"],
11            research=research,
12            audience=brief["audience"],
13            word_count=brief["target_length"],
14        )
15
16        # 3. Write sections
17        sections = []
18        for section in outline:
19            content = self.llm.write_section(
20                section=section,
21                context=research,
22                style=brief["style"],
23            )
24            sections.append(content)
25
26        # 4. Edit and refine
27        draft = "\n\n".join(sections)
28        edited = self.llm.edit(
29            content=draft,
30            guidelines=brief["style_guide"],
31        )
32
33        # 5. Generate metadata
34        metadata = self.llm.generate_metadata(edited)
35
36        return {
37            "content": edited,
38            "title": metadata["title"],
39            "description": metadata["description"],
40            "keywords": metadata["keywords"],
41        }