Agent 2 Agent
This is an idea to extend go-micro to support agents. It aligns with the evolving landscape of AI-powered distributed systems, where autonomous and goal-oriented components are becoming increasingly crucial.
Here's an outline of what `micro.NewAgent` and an `Agent` interface could encompass, its functionality, developer interaction, and an example:
### The `micro.NewAgent` and `Agent` Interface: Empowering Autonomous Components
The core idea is to provide a higher-level abstraction for building intelligent, self-managing, and often AI-driven components within a distributed system. While `micro.NewService` focuses on exposing functionalities (RPCs, Pub/Sub), `micro.NewAgent` would focus on defining an agent's *behavior* and its interaction with the environment and other services.
#### What would this encompass?
A `micro.NewAgent` would essentially wrap the capabilities of a `micro.Service` with additional features and a different lifecycle suited for autonomous operations. It would encapsulate:
1. **Identity and Discovery:** Like services, agents need to be discoverable within the go-micro ecosystem.
2. **State Management:** Agents often maintain internal state, which might need to be persistent or accessible across agent instances.
3. **Perception:** The ability to receive and interpret information from its environment (e.g., events, messages from other services, external data sources).
4. **Reasoning/Decision Making:** This is where the `genai` package becomes central. Agents would use AI models to process perceived information, formulate goals, and decide on actions.
5. **Action/Execution:** The ability to perform actions, which could involve calling other microservices, publishing events, interacting with external APIs, or modifying its own state.
6. **Learning/Adaptation (Optional but powerful):** Agents might learn from their experiences, updating their internal models or decision-making processes over time.
7. **Lifecycle Management:** How an agent starts, stops, rebalances, and recovers from failures in a distributed environment.
#### What kind of functionality would it have?
The `Agent` interface would expose methods crucial for building and managing intelligent agents:
* **`Init(...Option) error`**: Similar to `Service.Init`, for configuration and initialization of agent-specific components (e.g., AI model clients, state stores).
* **`HandleEvent(ctx context.Context, event micro.Event) error`**: A primary mechanism for agents to react to asynchronous events published on the message bus. This is where the agent's "perception" loop would often begin.
* **`ProcessRequest(ctx context.Context, req Request) (Response, error)`**: While agents are more autonomous, they might still expose RPC-like endpoints for direct queries or control from other services or human operators.
* **`Run() error`**: Starts the agent's main loop, which would involve listening for events, performing internal reasoning, and executing actions.
* **`Stop() error`**: Gracefully shuts down the agent.
* **`SetBrain(brain genai.Model)`**: Allows injecting a generative AI model into the agent for its reasoning capabilities.
* **`SetMemory(memory datastore.Store)`**: Provides a mechanism to attach a persistent memory store.
* **`Plan(ctx context.Context, goal string, contextData map[string]interface{}) ([]Action, error)`**: A potential method for an agent to formulate a series of actions based on a high-level goal and provided context. This would heavily leverage the `genai` package.
* **`Execute(ctx context.Context, action Action) error`**: A method to execute a planned action.
#### How would developers use and interact with it?
Developers would interact with `micro.NewAgent` in a similar fashion to `micro.NewService`, but with a shift in mindset towards defining intelligent behaviors rather than just API endpoints.
1. **Defining Agent Behavior:** Developers would implement the `Agent` interface, providing the concrete logic for how their agent perceives, reasons, and acts.
2. **Integrating with GenAI:** The `genai` package would be used within the agent's logic to interact with LLMs for tasks like:
* **Goal Interpretation:** Translating natural language goals into structured plans.
* **Contextual Understanding:** Analyzing incoming data and events to build a rich understanding of the current situation.
* **Decision Making:** Choosing the best course of action based on the current state and objectives.
* **Content Generation:** Producing human-readable responses, summaries, or new data.
3. **Tooling Integration:** Agents would likely expose "tools" (e.g., RPC methods on other services, external APIs) that the LLM can decide to use as part of its plan. The `genai` package often supports function calling, which would be a natural fit here.
4. **Observability:** Monitoring agent performance, decision paths, and interactions would be crucial. Go-micro's existing observability features (tracing, metrics) would be extended for agents.
### Outline of Main Features and Functionality
**`micro.NewAgent` Functionality:**
* **Agent Lifecycle Management:**
* `NewAgent(opts ...Option) Agent`: Creates a new agent instance.
* `Run()`: Starts the agent's event loop and processing.
* `Stop()`: Gracefully stops the agent.
* **Core Agent Capabilities:**
* **Perception:** Ability to subscribe to and process messages from the go-micro message bus (Pub/Sub), or receive direct RPC requests.
* **Reasoning Engine Integration:** Seamless integration with the `genai` package for large language model (LLM) interactions (text generation, chat, function calling).
* **Memory Management:** Pluggable interfaces for persistent and short-term memory stores (e.g., key-value stores, vector databases for RAG).
* **Tool Usage:** Mechanism for agents to discover and invoke other microservices or external APIs as "tools" to achieve their goals.
* **Autonomous Operation:** Handles continuous processing based on internal state, perceived events, and reasoned actions.
* **Distributed System Integration:**
* **Service Discovery:** Agents register themselves with the go-micro registry.
* **Load Balancing:** Client-side load balancing for agent-to-service communication.
* **Configuration:** Dynamic configuration loading for agent parameters (e.g., AI model parameters, memory settings).
* **Authentication/Authorization:** Secure communication between agents and other services.
**`Agent` Interface (Conceptual):**
```go
type Agent interface {
// Init initializes the agent with provided options.
Init(...Option) error
// Options returns the current agent options.
Options() Options
// Run starts the agent's main loop, processing events and executing actions.
Run() error
// Stop gracefully stops the agent.
Stop() error
// HandleEvent processes an incoming event from the message bus.
HandleEvent(ctx context.Context, event micro.Event) error
// HandleRequest processes a direct RPC-like request to the agent.
HandleRequest(ctx context.Context, req Request, rsp interface{}) error
// SetBrain sets the generative AI model for the agent's reasoning.
SetBrain(brain genai.Model)
// GetBrain returns the generative AI model used by the agent.
GetBrain() genai.Model
// SetMemory sets the memory store for the agent.
SetMemory(memory datastore.Store)
// GetMemory returns the memory store used by the agent.
GetMemory() datastore.Store
// Plan generates a plan of actions based on a goal and context.
Plan(ctx context.Context, goal string, contextData map[string]interface{}) ([]Action, error)
// Execute performs a specific action.
Execute(ctx context.Context, action Action) error
}
// Action represents a discrete action an agent can take.
type Action struct {
Name string `json:"name"`
Payload map[string]interface{} `json:"payload"`
}
```
### Example Usage: A Customer Support Agent
Let's imagine a "Customer Support Agent" that listens to incoming support tickets (events), uses a GenAI model to understand the issue, looks up knowledge base articles (via another service), and then drafts a response or escalates the ticket.
**1. Define the Agent's Core Logic (`support_agent.go`)**
```go
package main
import (
"context"
"fmt"
"log"
"time"
"go-micro.dev/v5"
"go-micro.dev/v5/broker"
"go-micro.dev/v5/client"
"go-micro.dev/v5/datastore"
"go-micro.dev/v5/genai" // Assuming the genai package is available
"go-micro.dev/v5/logger"
// Import necessary genai model and options if not directly exposed by go-micro/genai
// For demonstration, we'll use a placeholder for genai.Model
"github.com/google/generative-ai-go/genai/integration/gemini" // Placeholder, actual import might vary
)
// SupportTicket represents an incoming support ticket.
type SupportTicket struct {
ID string `json:"id"`
Customer string `json:"customer"`
Subject string `json:"subject"`
Issue string `json:"issue"`
Timestamp int64 `json:"timestamp"`
}
// SupportResponse represents the agent's drafted response or action.
type SupportResponse struct {
TicketID string `json:"ticket_id"`
AgentID string `json:"agent_id"`
ResponseType string `json:"response_type"` // e.g., "draft", "escalate", "resolve"
Content string `json:"content"`
SuggestedKBID string `json:"suggested_kb_id,omitempty"`
EscalatedTo string `json:"escalated_to,omitempty"`
}
// KnowledgeBaseService is an interface for interacting with a knowledge base service.
type KnowledgeBaseService interface {
Search(ctx context.Context, query string) (*KBArticle, error)
}
// KBArticle is a placeholder for a knowledge base article.
type KBArticle struct {
ID string `json:"id"`
Title string `json:"title"`
Content string `json:"content"`
}
// supportAgent implements the micro.Agent interface.
type supportAgent struct {
agent micro.Agent
brain genai.Model
kbSvc KnowledgeBaseService
// Other internal state like datastore, etc.
memory datastore.Store
}
func (sa *supportAgent) Init(opts ...micro.Option) error {
// Initialize agent, brain, and other dependencies
// The micro.NewAgent would internally call this.
return nil
}
func (sa *supportAgent) Options() micro.Options {
return sa.agent.Options()
}
func (sa *supportAgent) Run() error {
logger.Infof("Support Agent [%s] running...", sa.agent.Options().Name)
// The agent's main loop would be managed by micro.Agent.Run()
// We just need to define how it handles events.
return sa.agent.Run()
}
func (sa *supportAgent) Stop() error {
logger.Infof("Support Agent [%s] stopping...", sa.agent.Options().Name)
return sa.agent.Stop()
}
func (sa *supportAgent) HandleEvent(ctx context.Context, event micro.Event) error {
var ticket SupportTicket
if err := event.Decode(&ticket); err != nil {
logger.Errorf("Failed to decode support ticket event: %v", err)
return err
}
logger.Infof("Agent %s received ticket %s for customer %s: %s",
sa.agent.Options().Name, ticket.ID, ticket.Customer, ticket.Subject)
// 1. Perception & Reasoning (using genai)
prompt := fmt.Sprintf("Analyze the following customer support ticket and suggest a response or action. If a knowledge base article is relevant, suggest its ID. If escalation is needed, state the team to escalate to.\n\nTicket Subject: %s\nTicket Issue: %s\n\nSuggested Action:", ticket.Subject, ticket.Issue)
// In a real scenario, you'd use genai's chat session or function calling
// Here, we're simulating a simple text generation response
genaiResponse, err := sa.brain.GenerateContent(ctx, genai.Text(prompt))
if err != nil {
logger.Errorf("Failed to generate content with AI: %v", err)
return err
}
// Assuming the genaiResponse contains the structured action or text
// For simplicity, let's just take the first candidate's text
responseContent := "Unable to process with AI."
if len(genaiResponse.Candidates) > 0 && len(genaiResponse.Candidates[0].Parts) > 0 {
if text, ok := genaiResponse.Candidates[0].Parts[0].(genai.Text); ok {
responseContent = string(text)
}
}
// 2. Action (simulated based on AI response)
supportResp := SupportResponse{
TicketID: ticket.ID,
AgentID: sa.agent.Options().Name,
ResponseType: "draft", // Default
Content: responseContent,
}
// Simple keyword-based action detection for demonstration
if sa.kbSvc != nil {
// Try to find a relevant KB article
kbArticle, err := sa.kbSvc.Search(ctx, ticket.Issue)
if err != nil {
logger.Errorf("Failed to search knowledge base: %v", err)
} else if kbArticle != nil {
supportResp.SuggestedKBID = kbArticle.ID
supportResp.Content = fmt.Sprintf("Here's a draft response based on your issue and relevant knowledge base article:\n\n%s\n\nKnowledge Base Article: %s - %s", responseContent, kbArticle.Title, kbArticle.Content)
}
}
if sa.brain != nil && (sa.brain.GetInfo().Name == "gemini-pro" || sa.brain.GetInfo().Name == "gemini-1.5-pro-latest") { // Example check if it's a Gemini model
// More sophisticated parsing of AI response for structured actions
// This would ideally use function calling from genai
if containsIgnoreCase(responseContent, "escalate to technical team") {
supportResp.ResponseType = "escalate"
supportResp.EscalatedTo = "Technical Support"
} else if containsIgnoreCase(responseContent, "resolve automatically") {
supportResp.ResponseType = "resolve"
}
}
logger.Infof("Agent %s processed ticket %s. Suggested action: %s", sa.agent.Options().Name, ticket.ID, supportResp.ResponseType)
// Publish the response to another topic for a "response handler" service
return sa.agent.Options().Broker.Publish(ctx, "support.responses", &supportResp)
}
// HandleRequest is not used in this example but would be part of the Agent interface.
func (sa *supportAgent) HandleRequest(ctx context.Context, req client.Request, rsp interface{}) error {
logger.Infof("Support Agent [%s] received direct request for service %s, method %s", sa.agent.Options().Name, req.Service(), req.Method())
// Implement logic for direct queries to the agent, e.g., "What is your current status?"
return nil
}
func (sa *supportAgent) SetBrain(brain genai.Model) {
sa.brain = brain
}
func (sa *supportAgent) GetBrain() genai.Model {
return sa.brain
}
func (sa *supportAgent) SetMemory(memory datastore.Store) {
sa.memory = memory
}
func (sa *supportAgent) GetMemory() datastore.Store {
return sa.memory
}
// Plan and Execute would be more complex and depend on the agent's capabilities.
// For this example, we directly handle in HandleEvent.
func (sa *supportAgent) Plan(ctx context.Context, goal string, contextData map[string]interface{}) ([]micro.Action, error) {
// Not implemented for this simple example
return nil, nil
}
func (sa *supportAgent) Execute(ctx context.Context, action micro.Action) error {
// Not implemented for this simple example
return nil
}
func containsIgnoreCase(s, substr string) bool {
return len(s) >= len(substr) &&
fmt.Sprintf("%s", s)[0:len(substr)] == fmt.Sprintf("%s", substr)
}
// Placeholder for a simple KnowledgeBaseServiceClient
type knowledgeBaseServiceClient struct {
cl client.Client
}
func (kbc *knowledgeBaseServiceClient) Search(ctx context.Context, query string) (*KBArticle, error) {
// Simulate RPC call to a knowledge base service
fmt.Printf("Searching KB for: %s\n", query)
time.Sleep(100 * time.Millisecond) // Simulate network delay
if containsIgnoreCase(query, "billing") {
return &KBArticle{
ID: "KB001",
Title: "Understanding Billing Cycles",
Content: "This article explains our billing cycles and payment options.",
}, nil
}
return nil, nil
}
// NewSupportAgent creates a new supportAgent instance.
// This would be wrapped by micro.NewAgent internally.
func NewSupportAgent(a micro.Agent, brain genai.Model, kbSvc KnowledgeBaseService) micro.Agent {
sa := &supportAgent{
agent: a,
brain: brain,
kbSvc: kbSvc,
}
// The micro.NewAgent would likely expose options to set the brain and memory
// and then call an internal Init on the concrete agent implementation.
return a
}
```
**2. Main Go File (`main.go`)**
```go
package main
import (
"context"
"fmt"
"os"
"time"
"go-micro.dev/v5"
"go-micro.dev/v5/broker"
"go-micro.dev/v5/client"
"go-micro.dev/v5/logger"
"go-micro.dev/v5/server"
"github.com/google/generative-ai-go/genai"
genai_option "google.golang.org/api/option"
)
// main.go
func main() {
// Initialize Go Micro broker (e.g., in-memory for simple demo)
b := broker.NewBroker()
if err := b.Init(); err != nil {
logger.Fatalf("Broker Init error: %v", err)
}
if err := b.Connect(); err != nil {
logger.Fatalf("Broker Connect error: %v", err)
}
defer b.Disconnect()
// Initialize GenAI client
apiKey := os.Getenv("GEMINI_API_KEY")
if apiKey == "" {
logger.Fatal("GEMINI_API_KEY environment variable not set")
}
genaiCtx := context.Background()
genaiClient, err := genai.NewClient(genaiCtx, genai_option.WithAPIKey(apiKey))
if err != nil {
logger.Fatalf("Failed to create GenAI client: %v", err)
}
defer genaiClient.Close()
// Use a specific model (e.g., "gemini-pro")
model := genaiClient.GenerativeModel("gemini-pro")
if model == nil {
logger.Fatalf("Failed to get GenAI model 'gemini-pro'")
}
// Create a mock/placeholder for the KnowledgeBaseService client
kbServiceClient := &knowledgeBaseServiceClient{
cl: client.NewClient(), // Use default go-micro client
}
// Create a new agent instance using micro.NewAgent
// The micro.NewAgent would handle the core service registration and lifecycle.
// We'd then pass our custom agent implementation to it.
agentSvc := micro.NewAgent(
micro.Name("customer.support.agent"),
micro.Version("1.0.0"),
micro.Broker(b),
micro.WrapHandler(server.New
(func(next server.HandlerFunc) server.HandlerFunc {
return func(ctx context.Context, req server.Request, rsp interface{}) error {
logger.Infof("Agent received RPC: %s.%s", req.Service(), req.Method())
return next(ctx, req, rsp)
}
})),
)
// Create our concrete supportAgent handler
supportAgentHandler := &supportAgent{
agent: agentSvc, // The underlying micro.Agent instance
brain: model,
kbSvc: kbServiceClient,
memory: nil, // Placeholder for memory
}
// The `micro.NewAgent` constructor would likely accept an `Agent` interface
// and internally manage its lifecycle. For this example, we'll manually set handlers.
// This part is illustrative of how micro.NewAgent would bind the custom agent logic.
// In a real go-micro agent framework, you might have `agentSvc.Handle(supportAgentHandler)`.
// For demonstration, we'll just use the broker to listen for events,
// and let our supportAgentHandler implement the event handler directly.
// `micro.NewAgent` would likely internally wrap this.
// For simplicity, let's assume `micro.NewAgent` provides a `Subscribe` method
// or `HandleEvent` like `micro.NewService.Handle`.
// For now, let's directly subscribe to the broker from the agent instance.
// This is where the magic of "NewAgent" would provide a higher-level abstraction.
// For this mock-up, we are directly calling Init and Run on the agent service.
// The ideal scenario for go-micro/agent would be:
// agent := micro.NewAgent(...)
// agent.Handle(myAgentImplementation)
// agent.Run()
// A more realistic scenario for `micro.NewAgent` integration:
// A new type of Handler that takes `Agent` as a receiver.
// Or, the `NewAgent` function implicitly sets up the event subscription.
// For the purpose of this example, we'll directly set the handler logic
// within the agent's options or a custom `NewAgent` helper.
// Imagine `NewAgent` taking an `Agent` interface implementation.
// `agentSvc` *is* our `micro.Agent` instance. We need to make it process events.
// The `HandleEvent` and `HandleRequest` methods would be registered internally
// by the `micro.NewAgent` framework.
// For this mock, we manually subscribe to simulate the event handling.
_, err = b.Subscribe("customer.tickets", supportAgentHandler.HandleEvent)
if err != nil {
logger.Fatalf("Failed to subscribe to customer.tickets: %v", err)
}
// Initialise and run the agent
if err := agentSvc.Init(); err != nil {
logger.Fatalf("Agent initialization failed: %v", err)
}
// Manually set the brain and kbService onto the underlying supportAgentHandler
// This would be handled by `micro.NewAgent` options in a real implementation.
supportAgentHandler.SetBrain(model)
// No explicit SetMemory for now, but it would be here.
// Run the agent (which internally starts the service, broker, etc.)
if err := agentSvc.Run(); err != nil {
logger.Fatalf("Agent failed to run: %v", err)
}
}
// Helper to simulate publishing a ticket for testing
func publishTicket(b broker.Broker, ticket SupportTicket) {
ctx := context.Background()
if err := b.Publish(ctx, "customer.tickets", &ticket); err != nil {
logger.Errorf("Failed to publish ticket: %v", err)
} else {
logger.Infof("Published ticket: %s", ticket.ID)
}
}
// You could add another `main` function in a separate file or a test to
// simulate publishing tickets. For a single `main.go`, we'd add it here.
// To run this demonstration, you would typically:
// 1. Set GEMINI_API_KEY environment variable.
// 2. Run `go run main.go support_agent.go`.
// 3. In another terminal, simulate publishing a message (requires a go-micro client or similar).
// For a simplified, self-contained example, let's add a goroutine to publish a test ticket.
func init() {
go func() {
// Wait for the agent to start and subscribe
time.Sleep(5 * time.Second)
testTicket := SupportTicket{
ID: "TICKET-001",
Customer: "Alice Wonderland",
Subject: "Problem with my billing statement",
Issue: "My last bill seems incorrect. I was charged for an extra month.",
Timestamp: time.Now().Unix(),
}
publishTicket(broker.DefaultBroker, testTicket)
testTicket2 := SupportTicket{
ID: "TICKET-002",
Customer: "Bob The Builder",
Subject: "Technical issue with API integration",
Issue: "Our system is failing to connect to your payment gateway API. Getting 500 errors.",
Timestamp: time.Now().Unix(),
}
publishTicket(broker.DefaultBroker, testTicket2)
testTicket3 := SupportTicket{
ID: "TICKET-003",
Customer: "Charlie Chaplin",
Subject: "General inquiry about service features",
Issue: "Can you tell me more about the new streaming features?",
Timestamp: time.Now().Unix(),
}
publishTicket(broker.DefaultBroker, testTicket3)
}()
}
```
This outline and example demonstrate how `micro.NewAgent` and the `Agent` interface could provide a powerful abstraction for building intelligent, autonomous components within a go-micro distributed system, leveraging the new `genai` package for their reasoning capabilities. The key is to shift from purely request-response services to more proactive, event-driven, and AI-powered agents.
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