Autonomous AI Agents
Build AI systems that can reason, plan, and execute complex multi-step tasks independently. Agents go beyond simple Q&A—they take action in the real world.
Autonomous agents are AI systems that can independently pursue goals by breaking them into subtasks, using tools, and iterating until the objective is achieved. Instead of asking "What's the weather?", an agent can "Plan my weekend trip to somewhere sunny, book the cheapest flight, and add it to my calendar"—handling all the steps automatically.
With Spring AI, you can build agents that leverage function calling, memory, and reasoning patterns like ReAct and Chain-of-Thought to accomplish sophisticated tasks that would be impossible with simple prompt-response patterns.
What Makes an Agent?
Reasoning
Breaks down complex goals into actionable steps
Tool Use
Invokes external APIs, databases, and services
Iteration
Loops until the goal is achieved or limits hit
Observation
Evaluates results and adjusts strategy
Agent vs Chatbot
Chatbot
- • Single prompt → Single response
- • User drives the conversation
- • Limited to answering questions
- • May call 1-2 functions per turn
- • Stateless or simple memory
Autonomous Agent
- • Single goal → Many steps automatically
- • Agent drives toward objective
- • Takes actions and makes decisions
- • Chains many function calls
- • Rich memory and context tracking
The ReAct Pattern
ReAct (Reason + Act) is the most popular agent pattern. The model alternates between reasoning about what to do next and taking actions via tools.
Thought
"I need to find flights to warm destinations first"
Action
Call getWeather(["Miami", "Phoenix", "LA"])
Observation
Phoenix is 95°F, Miami is 85°F, LA is 72°F
Thought
"Phoenix is warmest. Now I'll search for flights."
Action
Call searchFlights(destination: "Phoenix")
Building an Agent with Spring AI
Agent Loop Implementation
Core pattern for an autonomous agent
@ServicepublicclassTravelPlannerAgent{privatefinalChatClient chatClient;privatestaticfinalintMAX_ITERATIONS=10;publicTravelPlannerAgent(ChatClient.Builder builder,WeatherFunction weatherFunction,FlightSearchFunction flightFunction,CalendarFunction calendarFunction){this.chatClient = builder
.defaultSystem("""
You are a travel planning agent. Your goal is to help users
plan trips by finding destinations, booking flights, and
managing their calendar.
Think step by step. After each action, evaluate the result
and decide what to do next. When the goal is complete,
respond with "TASK_COMPLETE:" followed by a summary.
""").defaultFunctions(weatherFunction, flightFunction, calendarFunction).build();}publicAgentResultexecute(String userGoal){List<String> steps =newArrayList<>();String currentContext = userGoal;for(int i =0; i <MAX_ITERATIONS; i++){// Agent thinks and actsString response = chatClient.prompt().user(currentContext).call().content();
steps.add("Step "+(i +1)+": "+ response);// Check if goal is completeif(response.contains("TASK_COMPLETE:")){returnnewAgentResult(true, steps,extractSummary(response));}// Update context for next iteration
currentContext ="Previous result: "+ response +"\n\nContinue working toward the goal: "+ userGoal;}returnnewAgentResult(false, steps,"Max iterations reached");}}recordAgentResult(boolean success,List<String> steps,String summary){}Designing Agent Tools
Agents are only as good as their tools. Well-designed tools have clear descriptions, handle errors gracefully, and return structured, actionable data.
@Component@JsonClassDescription("Search for available flights between cities")publicclassFlightSearchFunctionimplementsFunction<FlightQuery,FlightResults>{privatefinalFlightApiClient flightApi;@OverridepublicFlightResultsapply(FlightQuery query){try{List<Flight> flights = flightApi.search(
query.origin(),
query.destination(),
query.departureDate());// Return structured data the agent can reason aboutreturnnewFlightResults(true,
flights.size(),
flights.stream().sorted(Comparator.comparing(Flight::getPrice)).limit(5).toList(),null);}catch(Exception e){// Agents need to know when things failreturnnewFlightResults(false,0,List.of(), e.getMessage());}}}recordFlightQuery(@JsonProperty(required =true, description ="Origin city or airport code")String origin,@JsonProperty(required =true, description ="Destination city or airport code")String destination,@JsonProperty(description ="Departure date in YYYY-MM-DD format")String departureDate
){}recordFlightResults(boolean success,int totalFound,List<Flight> topFlights,String error
){}Agent Memory Patterns
Short-Term Memory
Tracks the current task's context, actions taken, and observations. Cleared after each goal is complete.
List<Message> scratchpad =newArrayList<>();
scratchpad.add(newUserMessage(goal));
scratchpad.add(newAssistantMessage(thought));
scratchpad.add(newToolMessage(result));Long-Term Memory
Persists user preferences, past interactions, and learned patterns across sessions using vector stores.
// Store important facts
vectorStore.add(newDocument("User prefers window seats",Map.of("type","preference")));// Retrieve relevant context
vectorStore.similaritySearch(goal);Agent Safety & Guardrails
Iteration Limits
Always cap the agent loop (10-20 iterations max). Infinite loops are expensive and dangerous.
Cost Controls
Track token usage per run. Set budget limits and abort if exceeded.
Action Approval
Require human confirmation for high-stakes actions (payments, deletions, external comms).
Sandboxing
Restrict tool capabilities. An agent shouldn't have production write access during testing.
Agent Use Cases
Research Agents
Gather information from multiple sources, synthesize findings, and produce reports.
DevOps Agents
Monitor systems, diagnose issues, and execute remediation playbooks automatically.
Task Automation
Handle multi-step workflows like onboarding, data migration, or report generation.