A little over a year ago, I built AIDA, the AI Diagnostic Agent, the very first autonomous agent I ever created. Her mission is to diagnose computer problems by querying the operating system through osquery. Even though AIDA went through a few updates over the months, I never really questioned her architecture until recently.
When I originally built AIDA in Python, models were at a different level of capability (gemini-2.5-flash at the time) and techniques like Agent Skills had not been invented yet. Because osquery exposes hundreds of tables that vary across macOS, Linux, and Windows, I built a local RAG solution powered by SQLite to feed schema definitions into the context on demand. Even though the dataset was small and static (one table per chunk, embedded as a one-off task), setting up a vector database for it was cumbersome, and the model didn’t always formulate the best search queries to the RAG tool, sometimes retrieving the wrong tables or missing a related table completely.
It wasn’t just the RAG setup, though. The entire codebase of AIDA had been hacked together rather than engineered, using early-stage “vibe coding”.
I have been saying this for over a decade, and it is even more true today: if your codebase is holding you back, nuke it and start over. Enduring the growth pains of v1 teaches you exactly what to simplify in v2. And while rewrites used to take months of effort and political capital, today you can iterate through three clean architectures before lunch.
After comparing the three main agent libraries in Go (the GenAI SDK, Genkit, and ADK), I decided to rebuild AIDA from scratch in Go using Genkit. Moving from Python and RAG to Genkit Go let me:
- Compile AIDA into a single, self-contained Go binary that runs either as a local CLI tool or as an HTTP service on Cloud Run.
- Replace the custom SQLite RAG pipeline with on-demand Agent Skills , giving the model the full catalog of OS-specific
osquerytables without relying on fuzzy search queries. - Enforce compile-time type safety from prompt inputs to structured outputs using
dotpromptand Go structs. - Evolve from a single-shot flow to stateful multi-agent delegation , isolating noisy system telemetry from web research.
Let’s walk through how each piece of the new architecture works in practice.
Setting up your coding agent and local workflow# #
Before writing any application code we need two things: the local genkit CLI for the development UI and command line utilities, and the Agent Skills that teach your coding agent how Genkit Go works.
Genkit CLI and Dev UI#
To install the genkit CLI run the official install script:
curl -sL cli.genkit.dev | bash
The genkit CLI is the backbone of local development in Genkit Go. If you run your program directly with go run ., it works as a normal Go binary, but by prefixing your run command with genkit start -- you will have access to the Genkit Developer UI (defaults to http://localhost:4000) where you can visually inspect and test the components of your Genkit program individually:
genkit start -- go run .
In the Dev UI, you can test registered flows, prompts, and agents interactively and inspect traces for prompts, tool calls, token usage, and latency.
Besides the Dev UI, the genkit CLI also provides command line helpers for executing flows, visualising traces and browsing the documentation. This last one is particularly useful to prevent model hallucination during the implementation phase.
Here are some examples:
genkit flow:run diagnose '{"userQuestion": "how is my disk usage?"}' --non-interactive -- go run .
genkit docs:list go
genkit docs:read go/middleware.md
Recommended skills#
The gold standard for SDKs nowadays is to ship them with official agent skills and Genkit is no different. If you are developing a Genkit Go program this is the one you are looking for:
developing-genkit-go: the official Genkit Go skill covering flows, dotprompt, built-in middleware, and experimental agents.
Since we are developing with Gemini models, it is also useful to install the gemini-api-dev skill:
gemini-api-dev: the official Gemini skill for current model IDs and thinking configuration.
You can install them into your workspace using the Vercel skills CLI:
npx skills add genkit-ai/skills --skill developing-genkit-go
npx skills add google-gemini/gemini-skills --skill gemini-api-dev
Or register the catalogs with kungfu for just-in-time (JIT) :
kungfu catalog add genkit-ai/skills
kungfu catalog add google-gemini/gemini-skills
If you prefer using an MCP server instead of skills, the CLI also ships an MCP server (genkit mcp). These days I personally have been leaning more towards skills, but MCPs still have value for certain niche use cases (e.g. documentation retrieval).
Core concepts# #
Genkit is an open-source framework for generative AI applications originally created by the Firebase team before growing into its own project. While its original language is JS, Genkit Go feels surprisingly idiomatic for seasoned Go developers, even if it still has a few rough edges here and there. The typical Genkit program won’t be much different than a traditional web service, thanks to adapters like genkit.Handler and familiar constructs like middleware and strongly typed flows and prompts.
Flows#
The fundamental unit in Genkit is a flow: a strongly typed function that wraps generative AI calls along with any pre- or post-processing logic. Let’s start AIDA as a single-shot diagnostic flow equipped with a tool to query the operating system via osqueryi:
type AIDARequest struct {
OS string `json:"os,omitempty" jsonschema_description:"The target operating system (defaults to runtime.GOOS)"`
UserQuestion string `json:"userQuestion" jsonschema_description:"The diagnostic question to investigate"`
}
type AIDAResponse struct {
Message string `json:"message" jsonschema_description:"The diagnostic findings and recommendations"`
}
type OsqueryInput struct {
Query string `json:"query" jsonschema_description:"The SQL query to execute in osquery."`
}
g := genkit.Init(ctx,
genkit.WithPlugins(&googlegenai.VertexAI{}),
genkit.WithDefaultModel("vertexai/gemini-3.8-flash"),
)
runOsquery := genkit.DefineTool(g, "runOsquery", "Runs a system inspection query using osquery.",
func(ctx *ai.ToolContext, in OsqueryInput) (string, error) {
out, err := exec.CommandContext(ctx, "osqueryi", "--json", in.Query).CombinedOutput()
if err != nil {
return "", fmt.Errorf("osqueryi failed: %w: %s", err, out)
}
return string(out), nil
},
)
diagnoseFlow := genkit.DefineFlow(
g,
"diagnose",
func(ctx context.Context, req AIDARequest) (AIDAResponse, error) {
if req.OS == "" {
req.OS = runtime.GOOS
}
resp, err := genkit.Generate(ctx, g,
ai.WithSystem(fmt.Sprintf("You are the Emergency Diagnostic Agent for diagnosing %s operating system failures. Your name is AIDA.", req.OS)),
ai.WithPrompt(req.UserQuestion),
ai.WithTools(runOsquery),
)
if err != nil {
return AIDAResponse{}, fmt.Errorf("diagnose flow failed: %w", err)
}
return AIDAResponse{Message: resp.Text()}, nil
},
)
genkit.DefineTool registers the runOsquery tool using the OsqueryInput struct to generate its JSON schema, while genkit.DefineFlow registers the flow by name with a callback function. If you look at the callback signature, it feels very similar to a typed HTTP handler — it takes a context.Context and a strongly typed request struct, and returns a typed response alongside a standard Go error.
Notice the g variable passed as the first argument to DefineTool, DefineFlow, and Generate. g is a *genkit.Genkit instance, which acts as the central registry for all flows, prompts, tools, models, and middleware in your program. You initialise g once in main with genkit.Init and pass it explicitly.
I’ll be honest, I’m not a huge fan of genkit.WithDefaultModel, as I prefer code to be explicit, but it does help reducing a bit of boilerplate if you are defining many flows. We are going to see a better option to hardcoding models in flows when discussing prompts.
genkit.Init sets up both the runtime registry and the observability stack. Every model and tool call inside a flow is automatically recorded as a child span under the flow’s trace. If your flow also performs custom pre- or post-processing — like querying a database or calling an external API — wrapping that logic in genkit.Run records it as its own named sub-step in the trace waterfall.
For development purposes you can run a flow via the CLI or in the Dev UI. In production, you may invoke flows as a regular Go function call (e.g. myFlow.Run), or exposed as an HTTP endpoint. Turning a flow into an HTTP handler takes a single line with genkit.Handler:
mux := http.NewServeMux()
mux.HandleFunc("POST /diagnose", genkit.Handler(diagnoseFlow))
port := os.Getenv("PORT")
if port == "" {
port = "3400"
}
// Note: "server" requires the server plugin:
// import "github.com/firebase/genkit/go/plugins/server"
log.Fatal(server.Start(ctx, "0.0.0.0:"+port, mux))
If you want to expose every flow registered in your application at once, you can iterate over genkit.ListFlows(g):
mux := http.NewServeMux()
for _, flow := range genkit.ListFlows(g) {
mux.HandleFunc("POST /"+flow.Name(), genkit.Handler(flow))
}
log.Fatal(server.Start(ctx, "0.0.0.0:"+port, mux))
Please note that genkit.Handler performs no authentication of its own, so wrap public endpoints in your standard net/http auth middleware.
Prompts#
Hardcoding prompts and model configuration in the flow code works for small projects, but it can get hard to maintain them as the project grows. Even if coding agents are doing most of the heavy work these days, we should not throw away years of engineering best practices, and code organisation is one of them.
Genkit has support for prompt templates using a format called dotprompt (*.prompt files). Prompts in dotprompt may contain more than just the instructions for the model; for example, they may include metadata for selecting and configuring the model, schema definitions, system instructions, and conversation history.
Extracting AIDA’s prompt, tools, and model configuration out of diagnoseFlow into prompts/aida.prompt looks like this:
---
model: vertexai/gemini-3.8-flash
tools:
- runOsquery
config:
thinkingConfig:
thinkingLevel: low
input:
schema:
os: string
userQuestion: string
output:
format: json
schema:
message: string
---
{{role "system"}}
You are the Emergency Diagnostic Agent for diagnosing {{os}} operating system failures. Your name is AIDA.
{{role "user"}}
{{userQuestion}}
By default, genkit.Init automatically loads .prompt files from ./prompts in your working directory. If you prefer to distribute your agent as a single binary, you can embed your prompts directory directly into the binary with //go:embed and pass it to genkit.WithPromptFS:
import "embed"
//go:embed prompts/*
var promptsFS embed.FS
func main() {
g := genkit.Init(context.Background(), genkit.WithPromptFS(promptsFS))
// ...
}
You can look up prompts in code via genkit.LookupPrompt and invoke them with myPrompt.Execute:
aida := genkit.LookupPrompt(g, "aida")
resp, err := aida.Execute(ctx, ai.WithInput(map[string]any{
"os": runtime.GOOS,
"userQuestion": "how is my disk usage?",
}))
If you have been writing Go for a while, that map[string]any probably bothers you as much as it bothers me. Fortunately, Genkit lets us bind the AIDARequest and AIDAResponse structs we defined earlier directly to aida.prompt using data prompts (genkit.LookupDataPrompt) and genkit.DefineSchemaFor:
// In prompts/aida.prompt, reference the Go types by name:
//
// input:
// schema: AIDARequest
// output:
// schema: AIDAResponse
//
// Register both schemas with Genkit so the prompt file can resolve them:
genkit.DefineSchemaFor[AIDARequest](g)
genkit.DefineSchemaFor[AIDAResponse](g)
aida := genkit.LookupDataPrompt[AIDARequest, *AIDAResponse](g, "aida")
resp, _, err := aida.Execute(ctx, AIDARequest{
OS: runtime.GOOS,
UserQuestion: "how is my disk usage?",
})
// resp is of type *AIDAResponse
// Note: The second return value is the raw *ai.ModelResponse
With DefineSchemaFor and LookupDataPrompt, you define your schema once in Go and keep full compile-time type safety from input to parsed output.
Plugins#
You extend Genkit with packages under the plugins/ namespace. While the most common case for a plugin is to register a model family, we also have plugins for broader capabilities like server and middleware. server is a special case in the sense it doesn’t need to be registered to be useful, but most plugins will be initialised in the genkit.Init call:
import (
"github.com/firebase/genkit/go/genkit"
"github.com/firebase/genkit/go/plugins/googlegenai"
"github.com/firebase/genkit/go/plugins/middleware"
)
g := genkit.Init(ctx,
genkit.WithPlugins(
&googlegenai.VertexAI{}, // Gemini via Vertex AI
&middleware.Middleware{}, // Registers Retry, Fallback, Skills, etc.
),
)
The concept of middleware should be familiar to most Go developers, but it is important to single out this plugin as it is the gateway for critical capabilities like fallbacks, retries and agent skills. Let’s have a deeper look at it next.
Replacing SQLite RAG with Agent Skills middleware# #
AIDA can already execute osquery commands through the runOsquery tool we defined earlier, but she still relies on the model’s general training weights to guess which tables and columns exist on each operating system (darwin, linux, and windows).
In the legacy Python version of AIDA, I solved this with a local SQLite RAG database where each chunk held a single table definition. While the dataset was small and static, the RAG approach was cumbersome to build and had a subtle flaw: gemini-2.5-flash had to guess what to search for first, and when its RAG query was slightly off, it would pull the wrong tables or miss a related table completely.
For the Go rewrite, I deleted the RAG pipeline and replaced it with Agent Skills. By pointing Antigravity at the osquery repository, I generated three OS-specific skills (darwin, linux, and windows) under ./skills, each containing the complete catalog of tables and schemas for that platform. Instead of hoping a vector search returns the right table, AIDA simply activates the skill for the current OS and gets the full schema reference on demand.
Genkit attaches middleware to generation calls using ai.WithUse, and the plugins/middleware package provides built-in implementations for retries, model fallbacks, and Agent Skills:
resp, err := genkit.Generate(ctx, g,
ai.WithSystem(fmt.Sprintf("You are AIDA, a %s operating system diagnostic assistant.", runtime.GOOS)),
ai.WithPrompt("My machine is running out of file descriptors. How do I inspect open files with osquery?"),
ai.WithTools(runOsquery),
ai.WithUse(
&middleware.Retry{MaxRetries: 3, InitialDelayMs: 1000, BackoffFactor: 2},
&middleware.Fallback{
Models: []ai.ModelRef{
googlegenai.ModelRef("vertexai/gemini-3.5-flash-lite", nil),
},
},
&middleware.Skills{
SkillPaths: []string{"./skills"},
},
),
)
Like HTTP middleware, ai.WithUse composes in order: placing Retry before Fallback retries the primary model before falling back to the secondary model.
When you point &middleware.Skills{} at ./skills, Genkit scans each subdirectory, injects the skill descriptions into the system prompt, and registers a use_skill tool so AIDA can load the right OS schema and query patterns on demand.
Please note that, unlike dotprompt, at the moment the Skills middleware does not support embedding filesystems, so you still need to resort to bundling a skills directory into your deployment.
Because we registered &middleware.Middleware{} during genkit.Init, we can also declare these middlewares directly inside prompts/aida.prompt:
---
model: vertexai/gemini-3.8-flash
tools:
- runOsquery
use:
- name: genkit-middleware/retry
config:
maxRetries: 2
- genkit-middleware/skills
---
For advanced use cases, you can also write custom middleware by implementing the ai.Middleware interface to intercept and wrap generation, model, or tool calls with your own logic.
The developer UI# #
You can find the complete code up to this point in the companion repository under github.com/danicat/gemini-for-go-developers/part-4/01-flow.
To test AIDA in the Genkit Developer UI, launch the application from that directory with genkit start:
export GOOGLE_CLOUD_PROJECT=your-project-id
export GOOGLE_CLOUD_LOCATION=global # required for Gemini 3.x
genkit start -- go run .
Open http://localhost:4000, select the diagnose flow from the sidebar, and run it with the following test payload:
{
"userQuestion": "what is my battery status?"
}
Here is the output of this prompt on my machine (observe the trace on the right panel showing that the skill was activated):
Stateful agents (experimental)# #
I typically like to stay clear of experimental features, but this one is an exception. While it’s perfectly possible to implement agents based on flows alone, developing more complex patterns — like chatbots, serial agents and parallel agents — does take a bit of effort crafting the right patterns using goroutines, synchronisation, error handling and others.
The introduction of the agent abstraction simplifies this process by providing us with a much more ergonomic API focused on the declarative aspect of agent creation instead.
Defining an agent#
Because the API is still experimental, you need to enable it with genkit.WithExperimental() when initialising Genkit. Here is how we turn AIDA into a stateful, multi-turn terminal agent backed by a session store:
import (
aix "github.com/firebase/genkit/go/ai/exp"
"github.com/firebase/genkit/go/ai/exp/localstore"
genkitx "github.com/firebase/genkit/go/genkit/exp"
)
g := genkit.Init(ctx,
genkit.WithExperimental(),
genkit.WithPlugins(&googlegenai.VertexAI{}, &middleware.Middleware{}),
)
store := localstore.NewInMemorySessionStore[struct{}]()
aida := genkitx.DefineAgent(g, "aida",
aix.InlinePrompt{
ai.WithModelName("vertexai/gemini-3.8-flash"),
ai.WithSystem(fmt.Sprintf("You are AIDA, a %s operating system diagnostic assistant. Use runOsquery to inspect the host.", runtime.GOOS)),
ai.WithTools(runOsquery),
ai.WithUse(&middleware.Skills{SkillPaths: []string{"./skills"}}),
},
aix.WithSessionStore(store),
)
// Interactive terminal session maintaining state across turns
scanner := bufio.NewScanner(os.Stdin)
var sessionID string
fmt.Print("AIDA> ")
for scanner.Scan() {
input := strings.TrimSpace(scanner.Text())
if input == "" || input == "exit" {
break
}
var opts []aix.InvocationOption[struct{}]
if sessionID != "" {
opts = append(opts, aix.WithSessionID[struct{}](sessionID))
}
out, err := aida.RunText(ctx, input, opts...)
if err != nil {
fmt.Fprintf(os.Stderr, "error: %v\n", err)
fmt.Print("AIDA> ")
continue
}
sessionID = out.SessionID
fmt.Printf("\n%s\n\nAIDA> ", out.Message.Text())
}
Storing out.SessionID and passing it into subsequent turns preserves the conversation history across the session. The [struct{}] type parameter simply tells Genkit that we only want to persist the message history without attaching custom session state.
The same techniques used to improve the code around flows can be used with agents. To avoid hardcoding the prompts, you can use genkitx.DefinePromptAgent to bind prompts/aida.prompt directly to the agent.
To expose AIDA over HTTP so a remote frontend can connect to it, genkitx.AllAgentRoutes mounts turn, snapshot, and abort endpoints onto standard Go routers:
mux := http.NewServeMux()
for _, route := range genkitx.AllAgentRoutes(g) {
mux.HandleFunc(route.Pattern(), route.Handler())
}
You can find the complete stateful agent implementation in the companion repository under github.com/danicat/gemini-for-go-developers/part-4/02-agent.
Multi-agent delegation#
There are cases where you will need more than one agent to perform a task. Benefits of multi-agent architecture include separation of concerns — to improve agent attention and reduce context rot — and parallelisation — to improve response times.
Using the experimental middlewarex.Agents middleware in Genkit, we can split AIDA’s investigative work between two specialists while keeping aida as the main coordinator persona:
inspector: Focuses exclusively on querying host telemetry viarunOsqueryand the OS-specificosqueryskills.researcher: Uses Google Search grounding to look up unfamiliar processes, binaries, error messages, and CVEs on the web without bloating AIDA’s main conversation history.
// requires:
// import (
// "google.golang.org/genai"
// middlewarex "github.com/firebase/genkit/go/plugins/middleware/exp"
// )
inspector := genkitx.DefineAgent(g, "inspector",
aix.InlinePrompt{
ai.WithModelName("vertexai/gemini-3.8-flash"),
ai.WithSystem(fmt.Sprintf("Inspect %s system telemetry using osquery and report diagnostic findings.", runtime.GOOS)),
ai.WithTools(runOsquery),
ai.WithUse(
&middleware.Retry{MaxRetries: 5},
&middleware.Skills{SkillPaths: []string{"./skills"}},
),
},
aix.WithDescription[struct{}]("Inspects host metrics, running processes, and system configuration using osquery."),
)
researcher := genkitx.DefineAgent(g, "researcher",
aix.InlinePrompt{
ai.WithModelName("vertexai/gemini-3.8-flash"),
ai.WithSystem("Research unfamiliar processes, binaries, error messages, and CVEs using Google Search and report concise findings."),
ai.WithConfig(&genai.GenerateContentConfig{
Tools: []*genai.Tool{
{GoogleSearch: &genai.GoogleSearch{}},
},
}),
},
aix.WithDescription[struct{}]("Searches the web for information on unfamiliar processes, binaries, error codes, and CVEs."),
)
aida := genkitx.DefineAgent(g, "aida",
aix.InlinePrompt{
ai.WithModelName("vertexai/gemini-3.8-flash"),
ai.WithSystem(fmt.Sprintf("You are AIDA, a %s operating system diagnostic assistant. Delegate host inspection to inspector and web research to researcher, then synthesize findings for the user.", runtime.GOOS)),
ai.WithUse(
&middleware.Retry{MaxRetries: 5},
&middlewarex.Agents{
Agents: []aix.AgentRef{inspector.Ref(), researcher.Ref()},
MaxDelegations: 5,
},
),
},
aix.WithSessionStore(store),
)
Delegation runs synchronously by default, though you can set Async: true on middlewarex.Agents if you want AIDA to kick off long-running specialist tasks in the background and join their results across turns.
One thing to keep in mind is the latency trade-off. When I timed the exact same "what is my battery status?" query against both versions (with thinkingLevel set to low), the single-agent version ( 02-agent) finished in 9.56s across 3 LLM calls, while this multi-agent version (03-multi-agent) took 17.02s across 5 LLM calls. The per-call latency is basically identical (~3.3s), so the extra ~7.5 seconds come purely from the two extra coordinator round-trips as aida delegates to inspector and then summarises its report.
Much like in parallel programming, the coordination overhead for a small N can make a simple task slower. For a toy query, a single agent is faster; in a real-world system, however, splitting an “uber-agent” into focused sub-agents is often worth those extra round-trips to isolate noisy tool outputs, optimise context usage, and improve overall response quality.
You can find the complete multi-agent example in the companion repository under github.com/danicat/gemini-for-go-developers/part-4/03-multi-agent.
Deploying to production# #
Runtimes#
Because a Genkit Go application compiles to a standard Go binary using net/http, there is no proprietary runtime lock-in. This means that basically any platform you use to host your HTTP servers is fair game for Genkit apps.
My first preference is always a serverless container runtime. At Google, I use primarily Google Cloud Run, only choosing more complex platforms (like Google Kubernetes Engine (GKE) or Gemini Enterprise) when I hit a critical blocker that requires a feature that Cloud Run doesn’t give to me.
It is also worth saying that in the past these types of decisions used to be more important than they are today. Replatforming took a lot of effort, so people had the tendency of wanting to “get it right” from the beginning. With coding agents we can adapt an app to a new platform in a matter of days (if not hours), so there are no more excuses to add complexity early to a project.
This is the theory. In practice, over the last year I never found a case that I couldn’t handle with Cloud Run. It is simply that convenient! The main exception is when you are building heavily around ADK and want managed session persistence and enterprise connectors out of the box, where the Gemini Enterprise Agent Platform (which we covered in Building AI Agents in Go) offers strong built-in synergies.
Now, typically agents — especially Gemini-based ones like Antigravity — don’t need help deploying to Cloud Run. But if your agent is being grumpy, you can always install the Cloud Run skill from github.com/google/skills to give it a nudge.
Observability#
During local development, genkit start records full execution traces and structured logs in .genkit/traces (which you should add to .gitignore) so you can inspect them in the Dev UI. In production, you want that same visibility into token usage, latency, and step-by-step traces in your monitoring backend.
Since Genkit is built on OpenTelemetry, you have two main options: if you are deploying to Google Cloud, the googlecloud plugin (or the firebase plugin, which runs the same exporter under the hood) sends everything to Cloud Logging, Cloud Trace, and Cloud Monitoring. If your team uses another backend like Datadog, Grafana, or Honeycomb, you can attach a standard OTLP exporter directly to the Genkit tracer provider instead.
For a typical Cloud Run deployment, enable the Google Cloud exporter before calling genkit.Init:
import "github.com/firebase/genkit/go/plugins/googlecloud"
func main() {
ctx := context.Background()
googlecloud.EnableGoogleCloudTelemetry(&googlecloud.GoogleCloudTelemetryOptions{
// Raw prompts and responses are logged by default; disable this for sensitive data:
DisableLoggingInputAndOutput: true,
// Set to true if you want to test Cloud export locally under `genkit start`:
ForceDevExport: false,
})
g := genkit.Init(ctx,
// if you are deploying to GCP, auth via Gemini Enterprise (aka Vertex AI) is preferred
genkit.WithPlugins(&googlegenai.VertexAI{}),
)
// ...
}
If you prefer an OTLP backend instead, register a standard OpenTelemetry span processor before genkit.Init and defer its shutdown function:
import (
"context"
"log"
"github.com/firebase/genkit/go/core/tracing"
"go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc"
sdktrace "go.opentelemetry.io/otel/sdk/trace"
)
func registerOTLP(ctx context.Context) func(context.Context) error {
// Configured via standard OTEL_EXPORTER_OTLP_* environment variables.
exp, err := otlptracegrpc.New(ctx)
if err != nil {
log.Fatalf("failed to build OTLP exporter: %v", err)
}
tp := tracing.TracerProvider()
tp.RegisterSpanProcessor(sdktrace.NewBatchSpanProcessor(exp))
return tp.Shutdown
}
Conclusions# #
Rebuilding AIDA from scratch is a great reminder of how fast AI engineering has matured over the past twelve months. What previously required a custom SQLite RAG pipeline, a local embedding model, and hundreds of lines of fragile Python glue code (a criticism of my own early vibe coding, not of Python itself) now compiles into a single, type-safe Go binary powered by Genkit and on-demand Agent Skills.
Moving to Genkit Go gave AIDA compile-time schema validation with dotprompt, built-in retries and skill injection through middleware, and clean multi-agent delegation, while keeping the snappy startup and deployment simplicity of a standard Go HTTP service. Paired with a fast model like Gemini 3.8 Flash, the difference in responsiveness compared to the legacy version is night and day.
In this article we focused on AIDA’s core runtime and backend architecture, reserving the frontend (using A2UI and generative interaction design) for a follow-up piece. If you are planning to build agents in Go, or if you have an early-stage RAG prototype that is starting to show its age, I highly recommend giving Genkit a try.