Building a Custom AI Agent with Go

Building My First AI Agent

The process of building a custom AI agent has become more accessible, with developers like Martin Cartledge creating their own agents using languages like Go. By following a tutorial by Thorsten Ball, Cartledge was able to build a functional agent, dubbed “GoldenEye”, which can read, write, and edit files within a working directory.

Why This Matters

The development of custom AI agents is significant because it highlights the importance of context in determining the effectiveness of these agents. While the underlying architecture of agents may be similar, the presentation and model used can greatly impact the user experience. Furthermore, the ability to build custom agents with relatively little code, as demonstrated by Cartledge, underscores the potential for companies to differentiate themselves through sleek UIs and performative models.

Key Insights

• The Anthropic SDK provides a straightforward way to create custom AI agents using Go: Cartledge’s implementation, GoldenEye, demonstrates this.
• Context is crucial in determining the effectiveness of AI agents: providing clear and precise input and output is essential for achieving desired outcomes.
• The models that power AI agents are a key differentiator: companies that provide high-quality, relevant, and dense models can shine in the market.

Working Example

// main.go
package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log"

	"github.com/anthropic/anthropic-sdk-go/anthropic"
)

type Agent struct {
	client *anthropic.Client
}

func NewAgent(client *anthropic.Client) *Agent {
	return &Agent{client: client}
}

func (a *Agent) Run(ctx context.Context, conversation []string) {
	// implementation details omitted
}

func main() {
	// create a new Anthropic client
	client, err := anthropic.NewClient("YOUR_API_KEY")
	if err != nil {
		log.Fatal(err)
	}

	// create a new agent
	agent := NewAgent(client)

	// run the agent
	agent.Run(context.Background(), []string{"hello", "world"})
}

Practical Applications

• Use Case: Companies like Anthropic and other AI startups can use custom agents to provide tailored solutions for their customers, leveraging the power of context and high-quality models.
• Pitfall: Failing to provide clear and precise input and output can lead to suboptimal performance and user experience, highlighting the importance of careful design and testing.

References:

• https://dev.to/lexingdailylife/building-my-first-ai-agent-lca
• https://anthropic.com/docs/api
• https://github.com/anthropic/goldeneye