第 2 回キャンペーンの突き合わせ結果を反映した。triage の state にコードで計算した 最新値の要約(now: プローブ、1 分 p95、1 分エラー率、再起動数、slo_now_ok)と 直近 5 分のエラーログ抜粋を加え、recovering は slo_now_ok を読むだけにした。 layer の choice を irreversible_needed の noul に置き換え、target_valid は カタログ action のときだけ尋ねる。JEV_MODE=enforce では review の 2 ルール (best_action 不一致かつ確信度 0.9 以上、target_valid 0.5 未満)で提案を escalate に格下げし、判断を res.jev.enforced に記録する。既定は shadow のまま。
331 lines
13 KiB
Go
331 lines
13 KiB
Go
// Package agent runs one diagnosis (experiment design 6.3/6.4): a Responses API tool loop over the read-only tools,
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// ending in a strict submit_diagnosis call. After the diagnosis it registers a layer-2 proposal with the harness and
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// hands it to the HITL bot, or sends the case to the escalation channel (design 6.5/6.6).
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package agent
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import (
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"bytes"
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"context"
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"encoding/json"
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"fmt"
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"log"
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"net/http"
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"strings"
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"time"
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"github.com/google/uuid"
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"github.com/selfheal-lab/selfheal-lab-apps/ai/internal/diag"
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"github.com/selfheal-lab/selfheal-lab-apps/ai/internal/harness"
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"github.com/selfheal-lab/selfheal-lab-apps/ai/internal/jev"
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"github.com/selfheal-lab/selfheal-lab-apps/ai/internal/openai"
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"github.com/selfheal-lab/selfheal-lab-apps/ai/internal/rag"
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"github.com/selfheal-lab/selfheal-lab-apps/ai/internal/tools"
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)
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type Config struct {
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Model string
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MaxTurns int
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MaxOutputTokens int
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SystemPrompt string // body of prompts/system.md
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Topology string // body of prompts/topology.md (design 1 IP table)
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SLO string // body of prompts/slo.md (design 4.2)
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HitlURL string // http://hitl:8081
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ProposalTTL time.Duration
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ConfidenceMin float64 // 0.5 (design 6.3)
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}
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type Agent struct {
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cfg Config
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oai *openai.Client
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tools *tools.Runner
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harness *harness.Client
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store *rag.Store
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enums diag.Enums
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promptV string
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http *http.Client
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jev *jev.Client // nil = shadow gates off
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jevEnforce bool // JEV_MODE=enforce: review rules may downgrade a proposal to escalate
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}
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func New(cfg Config, oai *openai.Client, tr *tools.Runner, hc *harness.Client, store *rag.Store, enums diag.Enums, promptVersion string) *Agent {
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if cfg.MaxTurns == 0 {
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cfg.MaxTurns = 12
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}
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if cfg.MaxOutputTokens == 0 {
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cfg.MaxOutputTokens = 4000
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}
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if cfg.ProposalTTL == 0 {
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cfg.ProposalTTL = 15 * time.Minute
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}
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if cfg.ConfidenceMin == 0 {
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cfg.ConfidenceMin = 0.5
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}
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return &Agent{cfg: cfg, oai: oai, tools: tr, harness: hc, store: store, enums: enums, promptV: promptVersion, http: &http.Client{Timeout: 30 * time.Second}}
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}
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// EnableShadow turns the Jev gates on (JEV_MODE=shadow records only; enforce also applies the review rules).
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func (a *Agent) EnableShadow(c *jev.Client, enforce bool) { a.jev, a.jevEnforce = c, enforce }
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// Request is the harness -> AI VM contract (harness alerts.go diagnose()).
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type Request struct {
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Alert map[string]any `json:"alert"`
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RunID *string `json:"run_id"`
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ScenarioID *string `json:"scenario_id"`
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}
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// Result is returned to the harness: the diagnosis fields at top level (the harness stores the whole object as the
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// diagnosis event payload and reads root_cause_label / proposed_action from it) plus bookkeeping.
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type Result struct {
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diag.Diagnosis
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SessionID string `json:"session_id"`
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Model string `json:"model"`
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PromptVersion string `json:"prompt_version"`
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Turns int `json:"turns"`
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ToolCalls int `json:"tool_calls"`
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DurationMs int64 `json:"duration_ms"`
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Disposition string `json:"disposition"` // proposal | escalated | none
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ProposalToken string `json:"proposal_token,omitempty"`
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Jev map[string]any `json:"jev,omitempty"` // shadow gate answers (recorded, never acted on)
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}
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type toolCallLog struct {
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Name string `json:"name"`
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Args string `json:"args"`
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Output string `json:"output"`
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Ms int64 `json:"ms"`
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}
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func (a *Agent) toolDefs() []openai.Tool {
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defs := tools.Definitions()
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defs = append(defs, openai.Tool{Type: "function", Strict: true, Name: "submit_diagnosis",
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Description: "Submit the final diagnosis. Call exactly once, after gathering evidence. This records the result; it does not execute anything.",
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Parameters: a.enums.Schema()})
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return defs
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}
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func (a *Agent) userMessage(ctx context.Context, req Request, prefetched string) string {
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if prefetched == "" {
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prefetched = a.Prefetch(ctx)
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}
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alert, _ := json.MarshalIndent(req.Alert, "", " ")
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var b strings.Builder
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b.WriteString("## Topology\n")
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b.WriteString(a.cfg.Topology)
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b.WriteString("\n\n## SLO\n")
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b.WriteString(a.cfg.SLO)
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b.WriteString("\n\n## Alert payload\n```json\n")
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b.Write(alert)
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b.WriteString("\n```\n\n## Last 10 minutes (pre-fetched)\n")
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b.WriteString(prefetched)
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if req.RunID != nil {
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fmt.Fprintf(&b, "\nrun_id=%s", *req.RunID)
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}
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if req.ScenarioID != nil {
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fmt.Fprintf(&b, " scenario_id=%s", *req.ScenarioID)
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}
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b.WriteString("\n\nInvestigate with the tools, then call submit_diagnosis exactly once.")
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return b.String()
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}
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// Prefetch returns the four pre-fetched 10-minute series (probe, p95, error ratio, legacy up) as markdown lines.
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func (a *Agent) Prefetch(ctx context.Context) string {
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var b strings.Builder
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for _, q := range []struct{ name, promql string }{
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{"probe_success", `probe_success`},
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{"api_p95_latency_s", `histogram_quantile(0.95, sum by (le) (rate(demo_http_request_duration_seconds_bucket[5m])))`},
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{"api_error_ratio", `sum(rate(demo_http_requests_total{status=~"5.."}[5m])) / sum(rate(demo_http_requests_total[5m]))`},
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{"legacy_up", `up{job="legacy-api"}`},
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} {
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out := a.tools.Call(ctx, "query_metrics", fmt.Sprintf(`{"promql":%q,"range_minutes":10}`, q.promql))
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if len(out) > 1200 {
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out = out[:1200] + "..."
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}
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fmt.Fprintf(&b, "- %s: %s\n", q.name, out)
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}
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return b.String()
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}
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// Diagnose runs the tool loop and returns the validated diagnosis. The session is stored whatever the outcome.
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func (a *Agent) Diagnose(ctx context.Context, req Request) (*Result, error) {
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return a.DiagnoseWith(ctx, req, "")
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}
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// DiagnoseWith runs Diagnose with an already pre-fetched sample block (empty = fetch now).
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func (a *Agent) DiagnoseWith(ctx context.Context, req Request, prefetched string) (*Result, error) {
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start := time.Now()
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sid := "ses-" + uuid.NewString()[:8]
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res := &Result{SessionID: sid, Model: a.cfg.Model, PromptVersion: a.promptV}
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var calls []toolCallLog
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input := []openai.Item{
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{Type: "message", Role: "system", Content: a.cfg.SystemPrompt},
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{Type: "message", Role: "user", Content: a.userMessage(ctx, req, prefetched)},
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}
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session := rag.Session{SessionID: sid, RunID: req.RunID, ScenarioID: req.ScenarioID, Model: a.cfg.Model, PromptVersion: a.promptV, Input: map[string]any{"alert": req.Alert, "user_message": input[1].Content}}
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finish := func(err error) {
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session.Output = res
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session.ToolCalls = calls
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session.Duration = time.Since(start)
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if err != nil {
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session.Error = err.Error()
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}
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if a.store != nil {
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if e := a.store.SaveSession(context.Background(), session); e != nil {
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log.Printf("session save: %v", e)
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}
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}
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}
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defs := a.toolDefs()
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var prevID string
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var diagnosis *diag.Diagnosis
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pf := false
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// usage is summed over every turn: with previous_response_id each turn re-bills the whole context, so the
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// last response alone understates the cost (seen 2026-10-08: 12 turns, last turn 11K input tokens).
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usage := map[string]int{"input_tokens": 0, "output_tokens": 0, "total_tokens": 0, "turns": 0}
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for turn := 1; turn <= a.cfg.MaxTurns && diagnosis == nil; turn++ {
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res.Turns = turn
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r := openai.Request{Model: a.cfg.Model, Input: input, Tools: defs, ToolChoice: "auto", MaxOutputTokens: a.cfg.MaxOutputTokens, ParallelToolCalls: &pf}
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if prevID != "" {
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r.PreviousResponseID = prevID
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}
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if turn == a.cfg.MaxTurns { // last chance: force the final call
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r.ToolChoice = map[string]any{"type": "function", "name": "submit_diagnosis"}
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}
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resp, err := a.oai.CreateResponse(ctx, r)
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if err != nil {
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finish(err)
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return nil, err
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}
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usage["input_tokens"] += resp.Usage.InputTokens
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usage["output_tokens"] += resp.Usage.OutputTokens
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usage["total_tokens"] += resp.Usage.TotalTokens
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usage["turns"]++
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prevID = resp.ID
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fcs := resp.FunctionCalls()
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if len(fcs) == 0 {
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// text only: nudge once towards the tool
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input = []openai.Item{{Type: "message", Role: "user", Content: "Use the tools to gather evidence and finish with submit_diagnosis."}}
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continue
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}
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input = input[:0]
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for _, fc := range fcs {
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if fc.Name == "submit_diagnosis" {
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d, err := diag.Parse(fc.Arguments, a.enums)
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if err != nil {
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input = append(input, openai.Item{Type: "function_call_output", CallID: fc.CallID, Output: fmt.Sprintf(`{"error":%q}`, err.Error())})
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calls = append(calls, toolCallLog{Name: fc.Name, Args: fc.Arguments, Output: "rejected: " + err.Error()})
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continue
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}
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diagnosis = &d
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calls = append(calls, toolCallLog{Name: fc.Name, Args: fc.Arguments, Output: "accepted"})
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break
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}
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t0 := time.Now()
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out := a.tools.Call(ctx, fc.Name, fc.Arguments)
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calls = append(calls, toolCallLog{Name: fc.Name, Args: fc.Arguments, Output: out, Ms: time.Since(t0).Milliseconds()})
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input = append(input, openai.Item{Type: "function_call_output", CallID: fc.CallID, Output: out})
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}
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}
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session.Usage = usage
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res.ToolCalls = len(calls)
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res.DurationMs = time.Since(start).Milliseconds()
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if diagnosis == nil {
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err := fmt.Errorf("no valid submit_diagnosis after %d turns", res.Turns)
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finish(err)
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return nil, err
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}
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res.Diagnosis = *diagnosis
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finish(nil)
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return res, nil
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}
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// Dispose applies design 6.3/6.5/6.6 after the diagnosis: proposal + HITL message, or escalation, or nothing.
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func (a *Agent) Dispose(ctx context.Context, req Request, res *Result) {
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d := res.Diagnosis
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diagMap := map[string]any{"root_cause_label": d.RootCauseLabel, "confidence": d.Confidence, "evidence": d.Evidence, "proposed_action": d.ProposedAction, "target": d.Target, "rationale": d.Rationale, "session_id": res.SessionID, "model": res.Model, "prompt_version": res.PromptVersion}
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switch {
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case d.ProposedAction == "none":
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res.Disposition = "none"
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return
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case d.ProposedAction == "escalate" || d.Confidence < a.cfg.ConfidenceMin:
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res.Disposition = "escalated"
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a.escalate(ctx, req, diagMap)
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return
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}
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target := targetFor(d.ProposedAction, d.Target)
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p, err := a.harness.Propose(ctx, req.RunID, req.ScenarioID, d.ProposedAction, target, diagMap, a.cfg.ProposalTTL)
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if err != nil {
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log.Printf("propose: %v", err)
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res.Disposition = "escalated"
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diagMap["note"] = "proposal registration failed: " + err.Error()
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a.escalate(ctx, req, diagMap)
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return
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}
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res.Disposition = "proposal"
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res.ProposalToken = p.Token
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body := map[string]any{"token": p.Token, "action_id": d.ProposedAction, "target": target, "diagnosis": diagMap, "run_id": req.RunID, "scenario_id": req.ScenarioID, "session_id": res.SessionID, "expires_in_s": p.ExpiresIn}
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if err := a.postJSON(ctx, a.cfg.HitlURL+"/post", body); err != nil {
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log.Printf("hitl post: %v", err)
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}
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}
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func (a *Agent) escalate(ctx context.Context, req Request, diagMap map[string]any) {
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runbook := ""
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if hits, err := a.tools.SearchRunbooks(ctx, fmt.Sprint(diagMap["root_cause_label"])+" "+fmt.Sprint(diagMap["rationale"]), 2); err == nil {
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if hs, ok := hits.([]rag.Hit); ok {
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var parts []string
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for _, h := range hs {
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parts = append(parts, "["+h.Source+"]\n"+h.Chunk)
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}
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runbook = strings.Join(parts, "\n\n")
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}
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}
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body := map[string]any{"diagnosis": diagMap, "runbook": runbook, "run_id": req.RunID, "scenario_id": req.ScenarioID, "alert": req.Alert}
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if err := a.postJSON(ctx, a.cfg.HitlURL+"/escalate", body); err != nil {
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log.Printf("hitl escalate: %v", err)
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}
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}
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func (a *Agent) postJSON(ctx context.Context, u string, body any) error {
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b, _ := json.Marshal(body)
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req, _ := http.NewRequestWithContext(ctx, "POST", u, bytes.NewReader(b))
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req.Header.Set("Content-Type", "application/json")
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resp, err := a.http.Do(req)
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if err != nil {
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return err
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}
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defer resp.Body.Close()
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if resp.StatusCode/100 != 2 {
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return fmt.Errorf("%s: status %d", u, resp.StatusCode)
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}
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return nil
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}
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// targetFor maps the free-text target of the diagnosis onto the harness action target keys (harness actions.go).
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func targetFor(actionID, target string) map[string]any {
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t := strings.TrimSpace(target)
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switch {
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case strings.HasPrefix(actionID, "k8s.rollout"), actionID == "k8s.scale":
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m := map[string]any{"namespace": "demo", "deployment": t}
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if actionID == "k8s.scale" {
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m["replicas"] = 3
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}
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if i := strings.Index(t, "/"); i > 0 {
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m["namespace"], m["deployment"] = t[:i], t[i+1:]
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}
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return m
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case actionID == "k8s.node_drain", actionID == "k8s.node_uncordon":
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return map[string]any{"node": t}
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case actionID == "gce.instance_reset":
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return map[string]any{"instance": t}
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case actionID == "mig.recreate_instance":
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return map[string]any{"instance": t}
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case actionID == "mig.resize":
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return map[string]any{"mig": "selfheal-legacy-mig", "size": 2}
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case actionID == "db.replay_resume":
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return map[string]any{"host": "db-replica"}
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}
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return map[string]any{"target": t}
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}
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