oikos/cmd/nomos/agent.go

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log/slog"
	"net/http"
	"os"
	"strings"
	"time"

	"github.com/google/uuid"
	"github.com/openai/openai-go"
	"github.com/openai/openai-go/option"
	"github.com/openai/openai-go/shared"
)

// maxIterations bounds one chat turn's tool-calling loop. Provisioning a
// service is a long chain (research → plan → run → per-step
// install/verify run calls), so this must be generous; a full deploy with the
// decomposed pct_create flow can legitimately need many steps. On exhaustion
// the loop now produces a real summary (finalSummary) rather than a dead end.
const maxIterations = 40
const maxLLMRetries = 3

// historyWindowSize bounds how many of a session's most recent persisted
// messages are replayed into the LLM's context on each turn — see
// store.go's getRecentMessages for why this exists (fix A2 of
// plans/2026-07-11-nomos-agent-code-review.md: unbounded history replay was
// a real, observed-in-production cost/latency/eventual-context-limit risk).
// 30 is a fixed-window choice, not token-budget-aware: simplest option that
// still keeps roughly the current task's working context, at the cost of
// occasionally dropping something a very long task still needed — the
// system note injected when truncation happens tells the model to check
// upsert_knowledge/search_knowledge rather than assume something didn't
// happen. A token-aware trim or LLM-summarize-on-drop are documented
// stretch options if a fixed window proves insufficient in practice.
const historyWindowSize = 30

var refusalDenylist = []string{
	"我没有相关信息",
	"您可以尝试问我其它问题",
	"我无法",
	"抱歉，我无法",
	"关于这个问题，我没有",
}

type agent struct {
	clients    *mcpClientPool // one MCP client PER SESSION, not shared — see mcpClientPool's doc comment
	system     string
	provider   *openai.Client
	model      string
	store      *store
	agentID    uuid.UUID
	reqOpts    []option.RequestOption
	apiBase    string // oikos HTTP API base, derived from NOMOS_MCP_URL, for chat-assent approvals
	apiToken   string // OIKOS_MCP_BEARER_TOKEN — api's combinedAuth requires it (no dev-open bypass)
	httpClient *http.Client
	// gate serializes turns per session (at most one in-flight turn per
	// sessionID). See turngate.go and plan 2026-08-03 F1.
	gate *turnGate
	// queue holds operator messages that arrived while a turn was already
	// running; they are auto-run when the gate frees (plan 2026-08-03 F2).
	// See messagequeue.go.
	queue *messageQueue
}

func newAgent(ctx context.Context, clients *mcpClientPool, st *store, agentSlug string, openrouterAPIKey string) (*agent, error) {
	system := loadSoul()
	apiKey := openrouterAPIKey
	model := os.Getenv("NOMOS_MODEL")
	if model == "" {
		// v4-pro over v4-flash: the flash tier over-narrates, occasionally
		// emits canned refusals, and is unreliable at multi-step tool use —
		// exactly the agentic provisioning path the operator needs to work.
		model = "deepseek/deepseek-v4-pro"
	}

	provider := openai.NewClient(
		option.WithBaseURL("https://openrouter.ai/api/v1"),
		option.WithAPIKey(apiKey),
	)

	agentID := st.resolveAgentID(ctx, agentSlug)
	if agentID == uuid.Nil {
		slog.Warn("nomos: agent entity not found; tool-call activity will not be logged", "slug", agentSlug)
	}

	// OpenRouter provider routing. data_collection=deny pins to zero-data-
	// retention providers (privacy: conversations + tool results transit
	// OpenRouter); require_parameters ensures the routed provider actually
	// supports tool calling. NOMOS_PROVIDER_SORT (price|throughput|latency)
	// and Exacto tool-accuracy routing are opt-in — the latter via a model
	// suffix in NOMOS_MODEL (e.g. "deepseek/deepseek-v4-flash:exacto"), so an
	// unsupported value never silently breaks the confirmed routing below.
	providerRouting := map[string]any{
		"data_collection":    "deny",
		"require_parameters": true,
	}
	if sort := os.Getenv("NOMOS_PROVIDER_SORT"); sort != "" {
		providerRouting["sort"] = sort
	}
	reqOpts := []option.RequestOption{option.WithJSONSet("provider", providerRouting)}

	// Derive the oikos HTTP API base from the MCP URL (e.g.
	// "http://api:8090/mcp?session_id=..." -> "http://api:8090"). Used for
	// chat-assent approvals, which call the same decision endpoint the UI's
	// Approve button calls.
	mcpURL := os.Getenv("NOMOS_MCP_URL")
	apiBase := ""
	if idx := strings.Index(mcpURL, "/mcp"); idx > 0 {
		apiBase = mcpURL[:idx]
	}

	return &agent{
		clients:    clients,
		system:     system,
		provider:   &provider,
		model:      model,
		store:      st,
		agentID:    agentID,
		reqOpts:    reqOpts,
		apiBase:    apiBase,
		apiToken:   os.Getenv("OIKOS_MCP_BEARER_TOKEN"),
		httpClient: &http.Client{Timeout: 15 * time.Second},
		gate:       newTurnGate(),
		queue:      newMessageQueue(),
	}, nil
}

func loadSoul() string {
	paths := []string{"/app/nomos/SOUL.md", "nomos/SOUL.md"}
	for _, p := range paths {
		if data, err := os.ReadFile(p); err == nil {
			return string(data)
		}
	}
	return `You are Nomos, the steward of the oikos — the AI agent for the hubris homelab.
You have access to MCP tools to query topology, health, knowledge, and request
gated mutations through run. Be concise. Prefer tools over guessing.`
}

// assentWindowDuration is how long after an operator approves a plan that
// config_mutation commands auto-run without re-approval. The operator
// approved the plan; the agent should execute it end-to-end without
// stopping every step to re-ask. Destructive actions still always need
// explicit typed confirmation regardless of the window.
const assentWindowDuration = 30 * time.Minute

// openAssentWindow records an active assent window in autonomy_settings so
// the MCP run tool (separate process) can check it before requiring approval
// for config_mutation commands. Key is scoped to this agent's UUID AND this
// session/task — see store.go's assentWindowActive for why: without the
// session dimension, approving one task's plan would silently auto-run
// unapproved actions in any other concurrently-running task.
func (a *agent) openAssentWindow(ctx context.Context, sessionID string) {
	if a.store == nil || a.store.pool == nil || a.agentID == uuid.Nil || sessionID == "" {
		return
	}
	key := assentWindowKey(a.agentID, sessionID)
	expires := time.Now().Add(assentWindowDuration).UTC().Format(time.RFC3339)
	_, err := a.store.pool.Exec(ctx,
		`INSERT INTO autonomy_settings (key, value) VALUES ($1, $2)
		 ON CONFLICT (key) DO UPDATE SET value = $2`, key, expires)
	if err != nil {
		slog.Warn("nomos: openAssentWindow", "error", err)
	} else {
		slog.Info("nomos: assent window opened", "agent", a.agentID, "session", sessionID, "expires", expires)
	}
}

type toolDef struct {
	Name        string         `json:"name"`
	Description string         `json:"description"`
	InputSchema map[string]any `json:"inputSchema"`
}

type agentEvent struct {
	Type      string `json:"type"`
	Data      any    `json:"data,omitempty"`
	SessionID string `json:"session_id,omitempty"`
	Iteration int    `json:"iteration,omitempty"`
	// IsThinking marks text/text_delta events that carry the model's internal
	// reasoning (text produced before tool calls in the same iteration), as
	// distinct from the final response text. The frontend renders these as
	// collapsible thinking blocks separated from the response.
	IsThinking bool `json:"is_thinking,omitempty"`
}

func (a *agent) chat(ctx context.Context, sessionID, message string, emit func(agentEvent)) {
	a.chatWith(ctx, sessionID, message, "", emit)
}

// chatWith is chat() with an optional system-injected note appended after the
// replayed history. The auto-continuation worker uses it to resume a session
// with a finished execution's result ("execution X completed: … — continue the
// plan") without persisting a fake user turn. message is normally the new user
// message; for a worker continuation it is empty and systemInject carries the
// note.
func (a *agent) chatWith(ctx context.Context, sessionID, message, systemInject string, emit func(agentEvent)) {
	correlationID := uuid.New().String()

	// emitError emits an error event followed by a done event. The done
	// event is CRITICAL on every terminal path: the frontend's
	// onComplete handler (chat.ts) treats a missing `done` as a severed
	// network connection and triggers an auto-reconnect → resumeSession.
	// Before this fix, a model empty-response (the most common case here)
	// returned without `done`, was misclassified as a network drop, and
	// the reconnect logic re-invoked the agent with a generic "report
	// your state" note — which caused the agent to re-propose the plan
	// and duplicate it in the sidebar (operator-reported 2026-07-14).
	// Every error return below must go through emitError so the frontend
	// shows the error inline instead of silently reconnecting.
	emitError := func(data string) {
		emit(agentEvent{Type: "error", Data: data, SessionID: sessionID})
		emit(agentEvent{Type: "done", Data: map[string]any{
			"session_id":     sessionID,
			"correlation_id": correlationID,
			"iterations":     0,
			"error":          true,
		}, SessionID: sessionID})
	}

	tools, err := a.buildTools(sessionID)
	if err != nil {
		emitError(fmt.Sprintf("build tools: %v", err))
		return
	}

	system := a.system
	if snapshot := a.fleetSnapshot(sessionID); snapshot != "" {
		system += "\n\n" + snapshot
	}
	messages := []openai.ChatCompletionMessageParamUnion{openai.SystemMessage(system)}
	history, truncatedHistory, _ := a.store.getRecentMessages(ctx, sessionID, historyWindowSize)
	if truncatedHistory {
		// Tell the model explicitly rather than silently dropping older
		// turns — otherwise it might assume something wasn't done just
		// because it doesn't see the turn that did it.
		messages = append(messages, openai.SystemMessage(fmt.Sprintf(
			"[System: this task has been running long enough that only the most recent %d turns of its history are included above your context — earlier turns happened but aren't shown. If you need to know what was already tried or found, check search_knowledge/get_entity_knowledge (if you recorded it) rather than assuming it didn't happen.]",
			historyWindowSize)))
	}
	// sawSetGoal / sawCompleteTask track whether this session has EVER framed
	// itself as a structured task (set_goal) or already reached a terminal
	// state (complete_task) — across both replayed history and this turn's
	// own tool calls (updated again below as they happen live). Used by the
	// end-of-turn safety net (plans/2026-07-11-task-completion-safety-net.md,
	// fix 1): most sessions are a single trivial Q&A exchange that answers in
	// text and never calls either tool, leaving agent_sessions.status stuck
	// at its creation-time default forever. If a session never framed itself
	// as a task, its first plain-text turn-end IS the task ending.
	var sawSetGoal, sawCompleteTask bool
	var lastAssistantCalls []persistedCall
	for _, m := range history {
		text := extractText(m.Content)
		switch m.Role {
		case "user":
			messages = append(messages, openai.UserMessage(text))
		case "assistant":
			if calls := extractToolCalls(m.Content); len(calls) > 0 {
				messages = append(messages, assistantToolCallMessage(calls))
				for _, c := range calls {
					messages = append(messages, openai.ToolMessage(c.resultText(), c.id))
					switch c.name {
					case "set_goal":
						sawSetGoal = true
					case "complete_task":
						sawCompleteTask = true
					}
				}
				lastAssistantCalls = calls
			}
			if text != "" {
				messages = append(messages, openai.AssistantMessage(text))
			}
		}
	}
	if len(history) == 0 {
		messages = append(messages, openai.UserMessage(message))
	}

	// Chat-assent approval: if the immediately-preceding assistant turn
	// proposed gated action(s) and the operator's new message reads as
	// authorization ("go ahead", "yes", ...), grant them now — this is the
	// primary approval path; the Approve button in the UI is a fallback for
	// when the operator wants to click instead of type. Destructive-risk
	// actions are never granted by loose assent — they need the stricter
	// isTypedConfirmation ("I confirm ...", per SOUL.md's guidance for what
	// to ask the operator to type).
	pending := extractPendingApprovals(lastAssistantCalls)
	assent := isAssent(message)
	typedConfirm := isTypedConfirmation(message)
	if len(pending) > 0 && (assent || typedConfirm) {
		var granted, blocked []string
		for _, p := range pending {
			if p.destructive && !typedConfirm {
				blocked = append(blocked, p.execID)
				continue
			}
			if !p.destructive && !assent {
				continue // typed-confirm alone doesn't grant a non-destructive item without also reading as assent
			}
			ok, status, aerr := a.approveExecution(ctx, p.execID)
			if aerr != nil {
				slog.Error("nomos: chat-assent approve", "execution", p.execID, "error", aerr)
				continue
			}
			if ok {
				granted = append(granted, p.execID)
				slog.Info("nomos: chat-assent granted", "execution", p.execID, "status", status, "session", sessionID)

				// An explicit typed confirmation for a destructive action
				// opens a short, target-scoped window so the rest of a
				// destructive recovery sequence on the SAME target (e.g.
				// stop -> destroy) doesn't need a second typed confirmation.
				if p.destructive && typedConfirm {
					if execUUID, perr := uuid.Parse(p.execID); perr == nil {
						if target := a.store.executionTarget(ctx, execUUID); target != "" {
							a.store.openDestructiveWindow(ctx, a.agentID, target, sessionID)
							slog.Info("nomos: destructive window opened", "agent", a.agentID, "target", target, "session", sessionID)
						}
					}
				}
			}
		}
		if len(granted) > 0 {
			a.openAssentWindow(ctx, sessionID)
			// Mark approved executions as continued so the continuation
			// worker doesn't call resumeSession while the chat handler is
			// still processing "go ahead" — two concurrent LLM calls for the
			// same session cause empty responses and race conditions.
			for _, execID := range granted {
				if execUUID, perr := uuid.Parse(execID); perr == nil {
					a.store.markContinued(ctx, execUUID)
				}
			}
			// No system note. The model already sees "go ahead" in the
			// replayed history (the user message was saved to the DB before
			// chat() was called). The old note said "they are now running"
			// which made the model think work was being done for it —
			// causing empty responses (finish_reason=stop, content_len=0).
			// The approved executions are dispatched; the model will
			// continue with the remaining plan steps naturally.
		}
		if len(blocked) > 0 {
			note := fmt.Sprintf("[System: execution(s) %s are classified DESTRUCTIVE and were NOT approved by loose assent — you must ask the operator for an explicit typed confirmation before they can run. Once they do confirm, further destructive steps on that SAME target (e.g. finishing a stop-then-destroy sequence) will auto-run for 15 minutes without asking again — but a different target always needs its own confirmation.]", strings.Join(blocked, ", "))
			messages = append(messages, openai.SystemMessage(note))
		}
	} else if assent && len(pending) == 0 {
		// The operator said "proceed"/"go ahead"/"yes" but there are no
		// pending approvals — the agent proposed a plan (via propose_plan)
		// and asked "shall I?" Open the assent window silently. No system
		// note: the model sees "go ahead" in the replayed history and
		// responds naturally.
		a.openAssentWindow(ctx, sessionID)
	}

	// Worker continuation: append the finished-execution note so the model
	// sees the result and decides the next step (proceed / recover / done).
	if systemInject != "" {
		messages = append(messages, openai.SystemMessage(systemInject))
	}

	// Retry cap (P0.1 from plans/2026-07-18-session-review-three-sessions.md):
	// track failing `run` calls within this turn so an identical command that
	// keeps failing is refused after maxRunRetries attempts. Without this,
	// session 1e9c7691 retried the same `chown` ~20 times, each retry piling
	// up a zombie process on the target (knfsd was holding a kernel lock).
	// The tracker is per-turn — a fresh turn after the operator responds can
	// retry once more, so this doesn't permanently block recovery.
	retries := newRunRetryTracker()

	for i := 0; i < maxIterations; i++ {
		params := openai.ChatCompletionNewParams{
			Model:    openai.ChatModel(a.model),
			Messages: messages,
			Tools:    tools,
		}

		var msg openai.ChatCompletionMessage
		var acc openai.ChatCompletionAccumulator

		// Capture token usage from this LLM response for activity logging.
	// Previously always NULL — every agent_activity row had no token
	// count. Now each tool call in this iteration gets the same total.
	totalTokens := 0

	for attempt := 0; attempt <= maxLLMRetries; attempt++ {
			acc = openai.ChatCompletionAccumulator{}
			stream := a.provider.Chat.Completions.NewStreaming(ctx, params, a.reqOpts...)
			for stream.Next() {
				chunk := stream.Current()
				acc.AddChunk(chunk)
				if len(chunk.Choices) > 0 {
					if delta := chunk.Choices[0].Delta.Content; delta != "" {
						emit(agentEvent{Type: "text_delta", Data: delta, SessionID: sessionID, Iteration: i + 1})
					}
				}
			}
			if err := stream.Err(); err != nil {
				if attempt < maxLLMRetries {
					slog.Warn("nomos: llm stream error, retrying", "error", err, "attempt", attempt+1, "session", sessionID)
					continue
				}
				emitError(fmt.Sprintf("llm: %v", err))
				return
			}
			if len(acc.Choices) == 0 {
				if attempt < maxLLMRetries {
					slog.Warn("nomos: no choices in response, retrying", "attempt", attempt+1, "session", sessionID)
					continue
				}
				emitError("no choices in response (the model returned zero completions — likely a provider or rate-limit issue)")
				return
			}

			msg = acc.Choices[0].Message
			finishReason := acc.Choices[0].FinishReason

			// Capture token usage from this iteration.
			if acc.Usage.TotalTokens > 0 {
				totalTokens = int(acc.Usage.TotalTokens)
			}

			if len(msg.ToolCalls) == 0 {
				if isRefusalOrEmpty(msg.Content) {
				if attempt < maxLLMRetries {
					slog.Warn("nomos: empty or refusal response, retrying",
						"session", sessionID, "iter", i+1, "attempt", attempt+1,
						"content_len", len(msg.Content), "finish_reason", finishReason)
					continue
				}
					// B.4: surface the real error context (finish_reason +
					// refusal text) instead of a generic "empty response" —
					// the operator can tell "content_filter — rephrase" from
					// "length — token limit hit" from "stop — model no-op'd".
					detail := "empty response"
					if msg.Refusal != "" {
						detail = fmt.Sprintf("refusal: %s", msg.Refusal)
					} else if finishReason != "" && finishReason != "stop" {
						detail = fmt.Sprintf("finish_reason=%s", finishReason)
					}
					emitError(fmt.Sprintf("Nomos returned an empty or unusable response (%s). Retry or rephrase.", detail))
					return
				}
			}
			break
		}

		if len(msg.ToolCalls) == 0 {
			emit(agentEvent{Type: "text", Data: msg.Content, SessionID: sessionID})
			if !sawSetGoal && !sawCompleteTask {
				a.autoCompleteTrivialTask(ctx, sessionID, msg.Content)
			}
			// Safety net: if the agent called set_goal (structured task)
			// but didn't call complete_task, and all plan steps are
			// terminal, auto-complete. The model often does the work but
			// forgets to close the loop (confirmed live: the #1 remaining
			// model reliability gap after D.1).
			if !sawCompleteTask {
				a.autoCompleteIfPlanDone(ctx, sessionID, msg.Content)
			}
			emit(agentEvent{Type: "done", Data: map[string]any{
				"session_id":     sessionID,
				"usage":          acc.Usage,
				"correlation_id": correlationID,
				"iterations":     i + 1,
			}, SessionID: sessionID})
			return
		}

		// P3: persist intermediate reasoning. When the model produces text
		// AND tool calls in the same iteration, the text is its reasoning
		// before the tool calls — the operator saw it live via text_delta,
		// but without emitting it as a `text` event here, the persist layer
		// (main.go/continue.go) never captures it and a reload shows only
		// the final summary + a flat tool-call list, not the thinking that
		// led to each step. Emitting it lets the persist layer accumulate
		// per-iteration reasoning into the row's text field.
		if strings.TrimSpace(msg.Content) != "" {
			emit(agentEvent{Type: "text", Data: msg.Content, SessionID: sessionID, IsThinking: true})
		}

		slog.Info("nomos: tool calls", "count", len(msg.ToolCalls), "iter", i+1, "correlation", correlationID)

		messages = append(messages, msg.ToParam())

		for _, tc := range msg.ToolCalls {
			var args map[string]any
			if err := json.Unmarshal([]byte(tc.Function.Arguments), &args); err != nil {
				args = map[string]any{}
			}

			switch tc.Function.Name {
			case "set_goal":
				sawSetGoal = true
			case "complete_task":
				sawCompleteTask = true
			}

			// Retry cap: if this `run` call has already failed
			// maxRunRetries times this turn with the same (target,
			// command), refuse to dispatch it again. Return a synthetic
			// tool result directing the agent to investigate *why* the
			// command hangs instead of retrying. See retrycap.go and
			// plans/2026-07-18-session-review-three-sessions.md P0.1.
			if tc.Function.Name == "run" {
				t, _ := args["target"].(string)
				c, _ := args["command"].(string)
				key := runFailureKey(t, c)
				if n := retries.failures(key); n >= maxRunRetries {
					directive := runRetryDirective(t, c, n)
					slog.Warn("nomos: run retry cap hit — refusing dispatch",
						"target", t, "failures", n, "session", sessionID)
					a.store.logActivity(ctx, a.agentID, sessionID, tc.Function.Name, args,
						tc.Function.Arguments, directive, 0, false, correlationID, totalTokens)
					emit(agentEvent{
						Type:      "tool_result",
						Data:      map[string]any{"name": tc.Function.Name, "result": directive, "id": tc.ID, "retry_capped": true},
						SessionID: sessionID,
						Iteration: i + 1,
					})
					messages = append(messages, openai.ToolMessage(directive, tc.ID))
					continue
				}
			}

			emit(agentEvent{
				Type:      "tool_use",
				Data:      map[string]any{"name": tc.Function.Name, "args": args, "id": tc.ID},
				SessionID: sessionID,
				Iteration: i + 1,
			})

			start := time.Now()
			// Session-scoped task tools are handled in-process; everything else
			// is forwarded to the shared MCP server.
			var result any
			var callErr error
			if localRes, handled := a.handleTaskTool(ctx, sessionID, tc.Function.Name, args); handled {
				result = localRes
			} else {
				// _session_id rides along on the wire call only — never in
				// `args` (which is what gets emitted/logged/persisted as the
				// model's own tool call) — so the MCP-side assent/destructive
				// window checks can scope to THIS task instead of bleeding
				// across every concurrently-running one sharing this agent
				// identity. Not part of any tool's declared InputSchema, so
				// the model never sees or supplies it.
				wireArgs := make(map[string]any, len(args)+1)
				for k, v := range args {
					wireArgs[k] = v
				}
				wireArgs["_session_id"] = sessionID
				var client *mcpClient
				client, callErr = a.clients.get(sessionID)
				if callErr == nil {
					result, callErr = client.callTool(tc.Function.Name, wireArgs)
				}
			}
			elapsed := int(time.Since(start).Milliseconds())

			inputJSON, _ := json.Marshal(args)
			inputStr := string(inputJSON)

			if callErr != nil {
				a.store.logActivity(ctx, a.agentID, sessionID, tc.Function.Name, args, inputStr, callErr.Error(), elapsed, false, correlationID, totalTokens)

				// Retry cap: dispatch errors (e.g. MCP client timeout)
				// count toward the cap too. A command that keeps timing
				// out at the gateway is exactly the pattern we want to
				// break — see session 1e9c7691's 20+ identical
				// `chown` timeouts.
				if tc.Function.Name == "run" {
					t, _ := args["target"].(string)
					c, _ := args["command"].(string)
					key := runFailureKey(t, c)
					n := retries.recordFailure(key)
					if n >= maxRunRetries {
						slog.Warn("nomos: run failure cap reached — next identical call will be refused",
							"target", t, "failures", n, "session", sessionID)
					}
				}

				emit(agentEvent{
					Type:      "tool_result",
					Data:      map[string]any{"name": tc.Function.Name, "error": callErr.Error(), "id": tc.ID},
					SessionID: sessionID,
					Iteration: i + 1,
				})
				messages = append(messages, openai.ToolMessage(callErr.Error(), tc.ID))
				slog.Error("nomos: tool error", "tool", tc.Function.Name, "error", callErr, "ms", elapsed)
				continue
			}

			resultJSON, _ := json.Marshal(result)
			a.store.logActivity(ctx, a.agentID, sessionID, tc.Function.Name, args, inputStr, string(resultJSON), elapsed, true, correlationID, totalTokens)

			// Link any execution this tool queued/started back to this
			// session, so the auto-continuation worker can feed its result
			// back here when it finishes (see cmd/nomos/continue.go). Async
			// executions (pct_create, apt_upgrade) are the ones that matter —
			// their result lands after this turn ends.
			for _, execID := range extractExecutionIDs(string(resultJSON)) {
				a.store.linkExecution(ctx, execID, sessionID)
			}

			// Record which entities this task touched (task —involves→ entity)
			// and pulse them on the live context panel. Args only — never
			// results — so a bulk query doesn't drag the whole fleet in.
			a.store.recordTouched(ctx, sessionID, tc.Function.Name, args)

			// When the agent records knowledge, link that note to this task so
			// the task's outcome view shows what it learned (and pulse it live).
			if tc.Function.Name == "upsert_knowledge" {
				a.store.linkKnowledgeToTask(ctx, sessionID, string(resultJSON))
			}

			emit(agentEvent{
				Type:      "tool_result",
				Data:      map[string]any{"name": tc.Function.Name, "result": result, "id": tc.ID},
				SessionID: sessionID,
				Iteration: i + 1,
			})
			messages = append(messages, openai.ToolMessage(string(resultJSON), tc.ID))
			slog.Info("nomos: tool success", "tool", tc.Function.Name, "ms", elapsed)

			// Retry cap: record failures of `run` calls so the cap above
			// can refuse a repeated identical failure. A "failure" here
			// means the dispatch errored OR the MCP result text matches
			// the "run on <target>: ERROR …" signature — both indicate
			// the command actually ran and failed, not just that it
			// queued for approval (pending approvals are not failures).
			// Pass the RAW result text (not JSON-encoded) so the helper's
			// HasPrefix check sees "run on …" not "\"run on …\"".
			if isRunFailure(tc.Function.Name, runResultText(result), callErr) {
				t, _ := args["target"].(string)
				c, _ := args["command"].(string)
				key := runFailureKey(t, c)
				n := retries.recordFailure(key)
				if n >= maxRunRetries {
					slog.Warn("nomos: run failure cap reached — next identical call will be refused",
						"target", t, "failures", n, "session", sessionID)
				}
			}

			// ask_operator pauses the task: the agent has posed a decision only
			// the operator can make. End the turn here so it doesn't barrel past
			// its own question — the answer (panel or chat reply) resumes it.
			// The prompt becomes the assistant's visible message so the question
			// also shows inline in the transcript.
			if tc.Function.Name == "ask_operator" {
				prompt, _ := args["prompt"].(string)
				emit(agentEvent{Type: "text", Data: prompt, SessionID: sessionID})
				emit(agentEvent{Type: "done", Data: map[string]any{
					"session_id":     sessionID,
					"correlation_id": correlationID,
					"iteration":      i + 1,
				}, SessionID: sessionID})
				return
			}
		}
	}

	// Hitting the step limit used to end the turn with a bare "max iterations
	// reached without final answer" — a dead end that made the operator ask
	// "status?" to find out what actually happened after a long working turn.
	// Instead, spend one final call asking the model to summarize what it did
	// and the current state, so the turn always ends with a real report.
	messages = append(messages, openai.SystemMessage("[System: you've reached the step limit for this turn. STOP calling tools now and write a concise status report: what you accomplished, the current state of the goal, anything that failed, and what remains. This is what the operator sees.]"))
	summary := a.finalSummary(ctx, messages)
	if summary == "" {
		summary = "I hit this turn's step limit while working. I've done a lot but couldn't wrap up cleanly — ask me for a status update and I'll summarize the current state."
	}
	emit(agentEvent{Type: "text", Data: summary, SessionID: sessionID})
	if !sawCompleteTask {
		a.autoCompleteIfPlanDone(ctx, sessionID, summary)
	}
	emit(agentEvent{Type: "done", Data: map[string]any{
		"session_id":     sessionID,
		"correlation_id": correlationID,
		"iterations":     maxIterations,
	}, SessionID: sessionID})
}

// finalSummary makes one non-tool LLM call to turn an exhausted tool-loop into
// a real status report instead of a dead-end message. Best-effort: empty on
// any error, and the caller has a fallback.
func (a *agent) finalSummary(ctx context.Context, messages []openai.ChatCompletionMessageParamUnion) string {
	params := openai.ChatCompletionNewParams{
		Model:    openai.ChatModel(a.model),
		Messages: messages,
		// No Tools: force a text answer.
	}
	resp, err := a.provider.Chat.Completions.New(ctx, params, a.reqOpts...)
	if err != nil || len(resp.Choices) == 0 {
		return ""
	}
	return resp.Choices[0].Message.Content
}

// extractText pulls the "text" field from a persisted message's JSONB content.
func extractText(content json.RawMessage) string {
	var m struct {
		Text string `json:"text"`
	}
	if err := json.Unmarshal(content, &m); err != nil {
		return ""
	}
	return m.Text
}

// persistedCall is one merged tool_use+tool_result pair from a persisted
// assistant message's tool_calls array. The store keeps them as two entries
// sharing the same id (mirroring the SSE event pair); replay needs one
// entry per id to build a valid tool-calling assistant message.
type persistedCall struct {
	id     string
	name   string
	args   json.RawMessage
	result json.RawMessage
	errMsg string
}

func (c persistedCall) resultText() string {
	if c.errMsg != "" {
		return c.errMsg
	}
	if len(c.result) > 0 {
		return string(c.result)
	}
	return "null"
}

// extractToolCalls parses and merges a persisted message's tool_calls array,
// preserving first-seen order across ids.
func extractToolCalls(content json.RawMessage) []persistedCall {
	var m struct {
		ToolCalls []struct {
			ID     string          `json:"id"`
			Type   string          `json:"type"`
			Name   string          `json:"name"`
			Args   json.RawMessage `json:"args"`
			Result json.RawMessage `json:"result"`
			Error  string          `json:"error"`
		} `json:"tool_calls"`
	}
	if err := json.Unmarshal(content, &m); err != nil || len(m.ToolCalls) == 0 {
		return nil
	}

	byID := make(map[string]*persistedCall, len(m.ToolCalls))
	var order []string
	for _, tc := range m.ToolCalls {
		if tc.ID == "" {
			continue
		}
		pc, ok := byID[tc.ID]
		if !ok {
			pc = &persistedCall{id: tc.ID}
			byID[tc.ID] = pc
			order = append(order, tc.ID)
		}
		if tc.Name != "" {
			pc.name = tc.Name
		}
		if len(tc.Args) > 0 && string(tc.Args) != "null" {
			pc.args = tc.Args
		}
		if tc.Type == "tool_result" {
			pc.errMsg = tc.Error
			pc.result = tc.Result
		}
	}

	calls := make([]persistedCall, 0, len(order))
	for _, id := range order {
		calls = append(calls, *byID[id])
	}
	return calls
}

// assistantToolCallMessage builds the tool-calling assistant message that
// must precede the tool-role results being replayed.
func assistantToolCallMessage(calls []persistedCall) openai.ChatCompletionMessageParamUnion {
	toolCalls := make([]openai.ChatCompletionMessageToolCallParam, 0, len(calls))
	for _, c := range calls {
		args := string(c.args)
		if args == "" {
			args = "{}"
		}
		toolCalls = append(toolCalls, openai.ChatCompletionMessageToolCallParam{
			ID: c.id,
			Function: openai.ChatCompletionMessageToolCallFunctionParam{
				Name:      c.name,
				Arguments: args,
			},
		})
	}
	return openai.ChatCompletionMessageParamUnion{
		OfAssistant: &openai.ChatCompletionAssistantMessageParam{ToolCalls: toolCalls},
	}
}

// fleetSnapshot returns a compact, current-as-of-now fleet health line for
// the system prompt so the agent starts each turn already oriented instead
// of spending its first iteration rediscovering topology it already has
// tools to query. Best-effort: an empty string on any failure just means no
// snapshot, not an error for the turn.
func (a *agent) fleetSnapshot(sessionID string) string {
	client, err := a.clients.get(sessionID)
	if err != nil {
		return ""
	}
	result, err := client.callTool("get_health_summary", map[string]any{})
	if err != nil {
		return ""
	}
	rows, ok := result.([]any)
	if !ok {
		return ""
	}

	counts := map[string]int{}
	var attention []string
	for _, r := range rows {
		row, ok := r.(map[string]any)
		if !ok {
			continue
		}
		health, _ := row["health"].(string)
		counts[health]++
		if health != "healthy" && health != "" {
			if slug, ok := row["slug"].(string); ok && len(attention) < 10 {
				attention = append(attention, fmt.Sprintf("%s(%s)", slug, health))
			}
		}
	}
	if len(counts) == 0 {
		return ""
	}

	summary := fmt.Sprintf("Current fleet snapshot (as of now): healthy=%d degraded=%d down=%d stale=%d unknown=%d.",
		counts["healthy"], counts["degraded"], counts["down"], counts["stale"], counts["unknown"])
	if len(attention) > 0 {
		summary += " Needs attention: " + fmt.Sprintf("%v", attention) + "."
	}
	return summary
}

// isRefusalOrEmpty returns true when the LLM response is blank or looks like a
// canned non-English refusal to an English-language conversation. Flash-tier
// models occasionally emit Chinese boilerplate deflection instead of a real
// answer; this catches it before it reaches the UI.
func isRefusalOrEmpty(text string) bool {
	if strings.TrimSpace(text) == "" {
		return true
	}
	ascii, nonASCII := 0, 0
	for _, r := range text {
		if r <= 127 {
			ascii++
		} else {
			nonASCII++
		}
	}
	if nonASCII > ascii {
		return true
	}
	for _, pattern := range refusalDenylist {
		if strings.Contains(text, pattern) {
			return true
		}
	}
	return false
}

func (a *agent) buildTools(sessionID string) ([]openai.ChatCompletionToolParam, error) {
	client, err := a.clients.get(sessionID)
	if err != nil {
		return nil, err
	}
	defs, err := client.listToolsFull()
	if err != nil {
		return nil, err
	}
	// Append nomos-local, session-scoped task tools (complete_task, …) to the
	// MCP tool list. They're routed to handleTaskTool, not the MCP client.
	defs = append(defs, taskToolDefs()...)

	var tools []openai.ChatCompletionToolParam
	for _, d := range defs {
		params := shared.FunctionParameters(d.InputSchema)
		if params == nil {
			params = shared.FunctionParameters{"type": "object", "properties": map[string]any{}}
		}

		tools = append(tools, openai.ChatCompletionToolParam{
			Type: "function",
			Function: shared.FunctionDefinitionParam{
				Name:        d.Name,
				Description: openai.String(d.Description),
				Parameters:  params,
			},
		})
	}
	return tools, nil
}

// listToolsFull returns the MCP server's tool list, cached on this client
// after the first call (see mcpClient.toolsCache). Fix F1 of
// plans/2026-07-11-nomos-agent-code-review.md: buildTools calls this at the
// start of every chat turn, including every auto-continuation resume — the
// tool list is static for the lifetime of one MCP connection, so re-fetching
// it every single time was avoidable network+parsing work on the hot path.
// Cache invalidates on reconnectLocked (an api restart may change what's
// registered).
func (c *mcpClient) listToolsFull() ([]toolDef, error) {
	c.toolsMu.Lock()
	if c.toolsCache != nil {
		cached := c.toolsCache
		c.toolsMu.Unlock()
		return cached, nil
	}
	c.toolsMu.Unlock()

	resp, err := c.doRequest("tools/list", map[string]any{})
	if err != nil {
		return nil, err
	}
	var tr struct {
		Tools []struct {
			Name        string         `json:"name"`
			Description string         `json:"description"`
			InputSchema map[string]any `json:"inputSchema"`
		} `json:"tools"`
	}
	if err := json.Unmarshal(resp.Result, &tr); err != nil {
		return nil, err
	}
	out := make([]toolDef, len(tr.Tools))
	for i, t := range tr.Tools {
		out[i] = toolDef{
			Name:        t.Name,
			Description: t.Description,
			InputSchema: t.InputSchema,
		}
	}

	c.toolsMu.Lock()
	c.toolsCache = out
	c.toolsMu.Unlock()
	return out, nil
}