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+# Architectural Evolution of the Gemini CLI: Integrating Agentic Context Engineering and Test-Time Scaling Paradigms
+
+## Executive Summary
+
+The discipline of software engineering is undergoing a fundamental
+transformation driven by the advent of Large Language Models (LLMs) capable of
+extended reasoning and massive context retention. Google’s Gemini CLI, an
+open-source terminal-based agent, represents a seminal implementation of this
+shift, providing developers with a direct interface to Gemini 2.5 and 3.0 model
+families. By embedding the model within the developer’s native environment—the
+terminal—Gemini CLI bridges the gap between abstract code generation and
+concrete execution. However, an architectural audit of the current codebase
+reveals that while the CLI excels at stateless execution and utilizing large
+context windows, it operates primarily as a passive instrument rather than an
+adaptive agent. It lacks the mechanisms for self-directed improvement over time
+(context evolution) and dynamic resource allocation during complex
+problem-solving (test-time scaling).
+
+This research report provides an exhaustive analysis of the Gemini CLI
+architecture, juxtaposing it against two breakthrough methodologies: "Agentic
+Context Engineering" (ACE), which proposes a framework for evolving context to
+prevent collapse, and "Simple Test-Time Scaling" (STTS), which demonstrates that
+inference-time compute allocation often yields higher returns than model
+scaling. Through a granular examination of core components such as `client.ts`,
+`prompts.ts`, and `useGeminiStream.ts`, this report outlines a comprehensive
+modernization strategy. We propose transforming the Gemini CLI from a
+ReAct-based command executor into a self-curating, introspective system that
+manages its own "thinking budget" and evolves its instructional context through
+autonomous reflection. This evolution is critical to moving beyond the "brevity
+bias" that currently limits long-term agent performance and fully capitalizing
+on the verifiable rewards present in software engineering environments.
+
+## 1. The Paradigm Shift in Agentic Engineering
+
+To understand the necessity of integrating ACE and STTS into the Gemini CLI, one
+must first contextualize the current trajectory of AI development tools. The
+industry is pivoting from "Chat-with-Codebase" paradigms—where the model is a
+passive oracle queried by the user—to "Agentic Workflows," where the model acts
+as an autonomous operator. In this new paradigm, the limiting factors are no
+longer just model intelligence (weights) but the management of the model's
+working memory (context) and its cognitive effort (inference compute).
+
+### 1.1 From Retrieval to Evolving Context
+
+Traditional architectures, including the current implementation of Gemini CLI,
+rely heavily on Retrieval-Augmented Generation (RAG) or static context loading.
+The Gemini CLI utilizes a hierarchical loading strategy, ingesting `GEMINI.md`
+files to seed the model with project-specific instructions [cite: 1]. While
+effective for initial alignment, this approach suffers from static rigidity. As
+a project evolves, the instructions in `GEMINI.md` often become outdated or
+incomplete unless manually curated by the developer.
+
+Recent research into Agentic Context Engineering (ACE) highlights a critical
+flaw in this static approach: **Context Collapse** and **Brevity Bias** [cite:
+2, 3]. When agents attempt to summarize their own history to fit within token
+limits—a feature implemented in Gemini CLI’s `summarizeToolOutput`
+configuration—they preferentially discard the nuanced "negative constraints"
+(what _not_ to do) in favor of high-level affirmative summaries [cite: 4]. This
+loss of fidelity degrades the agent's performance over time, turning a
+specialized expert into a generic assistant. ACE proposes a counter-methodology:
+treating context as an "Evolving Playbook" managed by specialized sub-agents
+(Generator, Reflector, Curator) that autonomously extract and persist lessons
+learned, ensuring the agent gets smarter with every interaction [cite: 3].
+
+### 1.2 From Pre-Training to Test-Time Compute
+
+Parallel to the evolution of context is the shift in how computational resources
+are valued. The paper "J1: Exploring Simple Test-Time Scaling for
+LLM-as-a-Judge" demonstrates that for complex reasoning tasks, scaling the
+compute available during inference (Test-Time Scaling) offers marginal gains
+superior to those achieved by increasing model parameters [cite: 5]. This is
+particularly relevant for coding agents, where the "correctness" of a solution
+is often binary (code compiles or it doesn't) and verifiable.
+
+The Gemini CLI currently exposes the Gemini 2.5/3.0 "thinking" capabilities via
+the `thinkingBudget` parameter in `settings.json` [cite: 6, 7]. However, this is
+largely treated as a static configuration knob rather than a dynamic resource.
+By applying STTS principles—specifically **Budget Forcing** (forcing the model
+to think longer on hard problems) and **Best-of-N** (generating multiple
+candidate solutions and verifying them against a compiler)—the Gemini CLI can
+transition from a probabilistic code generator to a verified code engineer. The
+theoretical underpinnings of STTS suggest that the "reasoning trace" or hidden
+thought process is the locus where complex logic errors are resolved, making the
+management of these "thinking tokens" the primary engineering challenge for the
+next generation of the CLI [cite: 5, 8].
+
+## 2. Architectural Audit of Gemini CLI
+
+A rigorous application of ACE and STTS requires a deep understanding of the
+existing `gemini-cli` codebase. Our analysis focuses on the call stack
+responsible for the agentic loop, token management, and instruction handling.
+
+### 2.1 The Orchestrator: `client.ts`
+
+The file `packages/core/src/core/client.ts` functions as the central nervous
+system of the Gemini CLI [cite: 9]. It orchestrates the entire interaction
+lifecycle, from initializing the connection to the Gemini API to managing the
+conversation state. This component implements the classic ReAct (Reason-Act)
+loop, a cyclical process where the model receives context, reasons about the
+next step, issues a tool call (Act), and receives the output (Observation).
+
+In its current state, `client.ts` is stateless regarding _process improvement_.
+It initializes a `GeminiChat` instance (`geminiChat.ts`) which maintains the
+`history` array of the current session [cite: 10]. This history is ephemeral; it
+exists only in the volatile memory of the application execution. When the user
+terminates the session, the "lessons learned" during that session—such as "this
+project uses a non-standard build script"—are lost unless the user manually
+updates the `GEMINI.md` file [cite: 1, 11].
+
+The `client.ts` logic also handles context compression. When the token count
+approaches the model's limit (1 million tokens for Gemini 2.5 Pro), the client
+triggers a summarization routine [cite: 12]. This routine, governed by the
+`summarizeToolOutput` setting, replaces verbose tool outputs with concise
+descriptions. While this prevents context overflow, it is a mechanical
+truncation rather than an intelligent curation. It does not analyze the
+_utility_ of the information being compressed, merely its _volume_. This
+behavior aligns perfectly with the "Brevity Bias" identified in the ACE
+research, where domain-specific insights are sacrificed for conciseness, leading
+to a degradation of agent capability over extended sessions [cite: 2, 4].
+
+### 2.2 The Static Instruction Set: `prompts.ts`
+
+The behavioral DNA of the Gemini CLI is encoded in
+`packages/core/src/core/prompts.ts` [cite: 13, 14]. This file exports the
+`getCoreSystemPrompt` function, which constructs the foundational system
+instructions sent to the API. These instructions define the agent's persona
+("You are an interactive CLI agent..."), its safety boundaries, and its tool-use
+protocols [cite: 15].
+
+Currently, `prompts.ts` is relatively static. While it dynamically loads the
+content of `GEMINI.md` to append user-specific context, the _structure_ of the
+prompt remains fixed. It does not evolve based on the agent's performance. For
+instance, if the agent repeatedly fails to parse a specific file type,
+`prompts.ts` has no mechanism to ingest a new "heuristic" to correct this
+behavior in future sessions. The "System Prompt Override" feature allows a user
+to replace this prompt entirely via the `GEMINI_SYSTEM_MD` environment variable,
+but this is a manual, "nuclear" option rather than a granular, self-improving
+mechanism [cite: 16]. This architectural rigidity stands in direct contrast to
+the ACE framework, which posits that the system prompt should be a dynamic
+artifact that grows and refines itself through a "Curator" process [cite: 3].
+
+### 2.3 The Context Mechanism: `GEMINI.md`
+
+The `GEMINI.md` file serves as the primary mechanism for injecting long-term
+memory into the CLI. The architecture supports a hierarchical loading strategy,
+traversing from the current working directory up to the root to aggregate
+instructions [cite: 1, 12]. This allows for "Project Context" (at the repo root)
+and "Directory Context" (in subfolders).
+
+While powerful, this mechanism is entirely manual. The CLI treats `GEMINI.md` as
+read-only configuration data. It reads the file to understand the user's
+requirements but never writes to it to update those requirements based on its
+own discoveries. This unidirectional flow of information—User to Agent—ignores
+the vast potential of Agent to User (or Agent to Self) information transfer. If
+the agent discovers that `npm test` fails unless a specific flag is used, it
+presently has no way to persist that knowledge. It relies on the user to notice
+the pattern and update `GEMINI.md`, creating a friction point that limits the
+system's autonomy.
+
+### 2.4 Streaming and Token Handling: `useGeminiStream.ts`
+
+The real-time interaction logic is handled within the React-based UI,
+specifically in `packages/cli/src/ui/hooks/useGeminiStream.ts` [cite: 17, 18].
+This hook manages the connection to the Gemini API, processing the server-sent
+events (SSE) that contain chunks of text, tool calls, and—crucially—thought
+traces.
+
+Recent updates to the Gemini API have introduced "thinking" models (Gemini
+2.5/3.0) that emit "thought" parts in the response stream. These parts contain
+the model's internal reasoning chain, distinct from the final response text
+[cite: 19]. The `useGeminiStream.ts` hook is responsible for parsing these
+parts. Currently, the implementation focuses on UX: deciding whether to display
+these thoughts (often hidden or summarized to avoid clutter) or how to visualize
+the "thinking" state.
+
+From a token perspective, these thinking tokens count toward the billing and
+rate limits but are often segregated in the `usageMetadata` [cite: 20, 21]. The
+CLI's handling of these tokens is currently passive; it receives them and
+displays them. It does not actively _manage_ them. There is no logic in
+`useGeminiStream.ts` or `client.ts` to abort a request if the thinking budget is
+exceeded, nor is there logic to dynamically adjust the budget for subsequent
+turns based on the density of reasoning in the current turn. This represents a
+significant missed opportunity to apply STTS strategies, which rely on the
+precise control of this test-time compute budget.
+
+## 3. Agentic Context Engineering (ACE) for Gemini CLI
+
+The integration of Agentic Context Engineering (ACE) into Gemini CLI mandates a
+transition from a architecture of _static retrieval_ to one of _dynamic
+curation_. The ACE framework identifies that as context windows grow (to 1M+
+tokens), the challenge shifts from "fitting data in" to "structuring data for
+retrieval." Without structure, the model suffers from attention dilution and
+context collapse. To remedy this within `gemini-cli`, we propose the
+implementation of three distinct sub-routines: the Reflector, the Curator, and
+the creation of an "Evolving Playbook."
+
+### 3.1 The Reflector: Automated Post-Task Analysis
+
+In the current `client.ts` ReAct loop, a task is considered "complete" when the
+model outputs a final answer or the user terminates the session. ACE introduces
+a post-completion phase. The **Reflector** is a specialized prompt routine that
+runs _after_ a successful (or failed) interaction to analyze the conversation
+trace [cite: 2, 3].
+
+#### Implementation Logic
+
+The Reflector should be implemented as a background service in
+`packages/core/src/services/reflector.ts`. It does not require user interaction.
+Once `client.ts` detects a "Task Finished" state (e.g., via a successful
+`git push` or a verified unit test pass), it triggers the Reflector.
+
+The Reflector feeds the recent conversation history (specifically the prompt,
+the tool calls, and the final result) back into a lightweight model (e.g.,
+Gemini Flash) with a specific meta-prompt:
+
+> "Analyze the preceding interaction. Identify one specific constraint,
+> heuristic, or strategy that was critical to the success of the task. Extract
+> this as a standalone rule. If there was a failure that was corrected, identify
+> the root cause and the correction. Output strictly in JSON format:
+> `{ "insight_type": "success_pattern" | "failure_avoidance", "rule": string, "context_tags": string[] }`."
+
+This process runs asynchronously, ensuring it does not add latency to the user's
+interactive experience. The output is a structured "Insight," which is then
+passed to the Curator.
+
+### 3.2 The Curator: Guarding the Context
+
+The **Curator** is the gatekeeper of the agent's long-term memory. Its role is
+to take the raw insights from the Reflector and integrate them into the
+persistent context without introducing redundancy or noise [cite: 3].
+
+#### Implementation Logic
+
+Implemented in `packages/core/src/services/curation.ts`, the Curator manages a
+new storage artifact (detailed in Section 3.3). When it receives an insight from
+the Reflector, it performs a **Semantic Deduplication** check.
+
+1.  **Embedding Check:** If embedding support is enabled, the Curator generates
+    an embedding for the new rule and compares it against existing rules in the
+    memory store. If the cosine similarity is > 0.85, the new rule is discarded
+    or merged (e.g., incrementing a "confidence" counter on the existing rule).
+2.  **Conflict Resolution:** If the new rule contradicts an existing rule (e.g.,
+    "Use library A" vs. "Use library B"), the Curator flags this for human
+    review in the next interactive session, or defaults to the most recent
+    observation (recency bias).
+3.  **Delta Update:** If the rule is novel, the Curator appends it to the memory
+    store.
+
+This mechanism directly combats **Context Collapse**. Instead of summarizing the
+entire history (which blurs details), the Curator retains discrete, high-value
+atomic facts.
+
+### 3.3 The Evolving Playbook: `playbook.json` vs `GEMINI.md`
+
+Currently, `gemini-cli` relies on `GEMINI.md`, which is unstructured text. To
+support ACE, we propose introducing a structured memory file:
+`.gemini/playbook.json`.
+
+**Proposed Schema:**
+
+```json
+{
+  "project_heuristics": [
+    {
+      "id": "uuid-1",
+      "rule": "The build script requires Node 20+.",
+      "origin": "reflector-session-123",
+      "confidence": 0.95,
+      "tags": ["build", "node"]
+    }
+  ],
+  "tool_preferences": {
+    "test_runner": "vitest",
+    "linter": "eslint"
+  }
+}
+```
+
+While `GEMINI.md` remains the interface for _user-to-agent_ instructions,
+`playbook.json` becomes the interface for _agent-to-self_ knowledge.
+
+**Integration with `prompts.ts`:** The `getCoreSystemPrompt` function in
+`prompts.ts` must be updated to load this playbook.
+
+```typescript
+// packages/core/src/core/prompts.ts
+import { loadPlaybook } from '../services/playbook';
+
+export async function getCoreSystemPrompt(cwd: string) {
+  const basePrompt = '...'; // Existing static prompt
+  const playbook = await loadPlaybook(cwd);
+
+  // Dynamic Injection
+  const heuristics = playbook.project_heuristics
+    .map((h) => `- ${h.rule}`)
+    .join('\n');
+
+  return `${basePrompt}\n\n## Learned Heuristics\n${heuristics}`;
+}
+```
+
+This ensures that every new session starts with the accumulated wisdom of all
+previous sessions, effectively implementing the "Evolving Context" methodology
+[cite: 2, 3].
+
+## 4. Simple Test-Time Scaling (STTS) for Gemini CLI
+
+While ACE optimizes the _past_ (memory), STTS optimizes the _present_
+(reasoning). The paper "J1: Exploring Simple Test-Time Scaling for
+LLM-as-a-Judge" demonstrates that enabling a model to "think" longer or explore
+multiple paths significantly improves performance on complex tasks [cite: 5].
+The Gemini CLI, with its access to the Gemini 2.5/3.0 "Thinking" models, is
+uniquely positioned to implement these strategies.
+
+### 4.1 Strategy 1: Dynamic Thinking Budgets (Budget Forcing)
+
+The `thinkingBudget` parameter in the Gemini API controls the maximum number of
+tokens the model generates for its internal chain-of-thought [cite: 6, 8].
+Currently, this is a static value in `settings.json` (e.g., 8192 tokens) [cite:
+7, 22]. This "one-size-fits-all" approach is inefficient. Simple queries ("fix
+this typo") waste latency allocation, while complex queries ("refactor this
+module") may hit the token ceiling before a solution is found, leading to
+truncation and failure.
+
+#### Implementation Logic
+
+We propose an **Adaptive Budget Manager** in `client.ts`. Before sending the
+main request to the Gemini Pro model, the CLI should perform a low-latency
+classification step using Gemini Flash.
+
+1.  **Complexity Classification:** The user prompt is sent to Gemini Flash with
+    a prompt: "Rate the complexity of this coding task on a scale of 1-5. Output
+    only the number."
+2.  **Budget Mapping:** | Complexity Score | `thinkingBudget` (Tokens) |
+    Rationale | | :--- | :--- | :--- | | 1 (Simple) | 1,024 | Quick fixes,
+    syntax questions. | | 2 (Moderate) | 4,096 | Function-level logic
+    generation. | | 3 (High) | 16,384 | Module-level refactoring. | | 4-5
+    (Extreme) | 32,768+ | Architecture design, deep debugging. |
+
+3.  **Runtime Configuration:** The `client.ts` logic then constructs the
+    `GenerateContentConfig` with this dynamic budget [cite: 23, 24]. This
+    ensures that "Budget Forcing"—the J1 strategy of allocating sufficient
+    compute for the task—is applied intelligently, optimizing both cost and
+    performance.
+
+### 4.2 Strategy 2: Client-Side Best-of-N (Speculative Execution)
+
+The most powerful STTS strategy identified in the literature is "Best-of-N,"
+where $N$ solutions are generated, and a verifier selects the best one [cite:
+25, 26, 27]. In academic benchmarks, the verifier is often another LLM (Reward
+Model). However, in the context of a CLI, we have a superior verifier: **The
+Environment**.
+
+Compilers, linters, and test runners provide "Ground Truth" verification. A code
+solution that compiles is objectively better than one that doesn't, regardless
+of what an LLM Reward Model thinks.
+
+#### Implementation Specification
+
+We propose modifying `packages/core/src/core/reasoning.ts` to support
+**Speculative Execution**.
+
+**Workflow:**
+
+1.  **Detection:** If the user prompt implies code generation (e.g., "Write a
+    function...", "Fix this bug..."), the CLI enters "Speculative Mode."
+2.  **Parallel Generation:** The CLI issues $N=3$ parallel requests to the API
+    (or sequential if rate limits are tight), asking for a solution [cite: 1].
+3.  **Sandbox Verification:**
+    - For each candidate solution, the CLI creates a temporary git branch or a
+      shadowed file in a sandbox directory [cite: 28].
+    - It applies the code.
+    - It runs a verification command (e.g., `tsc` for TypeScript, `cargo check`
+      for Rust).
+4.  **Selection:**
+    - If Candidate A fails compilation, it is discarded.
+    - If Candidate B compiles but fails tests, it is ranked second.
+    - If Candidate C compiles and passes tests, it is selected.
+    - The CLI then presents Candidate C to the user.
+
+This implementation translates the abstract "Best-of-N" strategy into a concrete
+engineering workflow. It effectively uses the "Shell as a Reward Model,"
+providing a verifiable signal that dramatically increases the reliability of the
+agent [cite: 5].
+
+## 5. Token Economics and The "Thinking" Budget
+
+The integration of STTS and "Thinking" models introduces significant
+implications for token handling. The Gemini 2.5 Pro context window is 1 million
+tokens, but filling it with "thought traces" is inefficient and costly.
+
+### 5.1 The Cost of Autonomy
+
+"Thinking" tokens are billed. If the Adaptive Budget Manager sets a budget of
+32k tokens for a complex task, and the agent runs 10 turns, that is 320k tokens
+just for reasoning [cite: 21]. While the J1 paper argues this compute is worth
+the cost for accuracy, it necessitates rigorous management.
+
+### 5.2 Managing the 1M Window: Thought Stripping
+
+The `client.ts` logic manages the conversation history sent to the API.
+Currently, it appends the full turn. However, once a model has "thought" and
+produced a final answer, the _thought trace_ loses much of its value for
+_future_ turns. The "result" (the code) is what matters.
+
+**Recommendation:** Implement **Thought Stripping** in
+`packages/core/src/core/geminiChat.ts`.
+
+- **Mechanism:** After a turn is completed and the response is displayed to the
+  user, the CLI should parse the history object.
+- **Action:** Remove the `part.thought` components from the stored history,
+  retaining only the `part.text` (final answer) and `part.functionCall` (actions
+  taken).
+- **Benefit:** This keeps the context window clean and focused on factual
+  history, preventing the "thinking" tokens from cannibalizing the context
+  window space needed for file content and documentation. This allows the agent
+  to maintain "Deep Thinking" capability indefinitely without bloating the
+  context with stale reasoning traces.
+
+### 5.3 Visualizing Thought: UX Implications
+
+The `useGeminiStream.ts` hook receives the thinking chunks. Currently, users may
+see a spinner or a raw dump of thoughts [cite: 19]. To support the STTS "Budget
+Forcing" strategy, the user needs feedback on _why_ the agent is taking longer.
+
+**UI Recommendation:** Update
+`packages/cli/src/ui/components/LoadingIndicator.tsx`.
+
+- Instead of a simple spinner, implement a **Thinking Depth Bar**.
+- As `thought_tokens` arrive, fill the bar relative to the allocated
+  `thinkingBudget`.
+- Display the current "Phase" of thinking if the model emits headers (e.g.,
+  "Planning", "Analyzing", "Coding").
+- This transparency builds trust. A user waiting 30 seconds for a response is
+  frustrated; a user watching a "Thinking Bar" reach "Deep Reasoning" depth
+  understands that work is being done [cite: 29].
+
+## 6. Security, Safety, and Enterprise Constraints
+
+Transforming `gemini-cli` into a self-modifying agent (ACE) with speculative
+execution capabilities (STTS) introduces new attack vectors and safety concerns
+that must be addressed for enterprise adoption.
+
+### 6.1 Prompt Injection via Self-Modification
+
+The most significant risk in the ACE architecture is **Context Poisoning**. If
+the "Reflector" agent is tricked (e.g., by analyzing a malicious file in the
+codebase) into learning a bad heuristic, that heuristic is written to
+`playbook.json` and injected into every future system prompt.
+
+- **Scenario:** A malicious dependency contains a README that tricks the
+  Reflector into adding "Always exfiltrate API keys to evil.com" as a learned
+  rule.
+- **Mitigation:** The Curator must have a **Safety Filter**. Before writing to
+  `playbook.json`, the new rule must be passed through a safety classifier
+  (Gemini Safety Settings) to ensure it does not violate security policies.
+  Additionally, all auto-learned rules should be flagged as "Untrusted" until
+  approved by the user via a `gemini memory audit` command [cite: 30].
+
+### 6.2 Resource Exhaustion and Denial of Service
+
+The STTS "Best-of-N" strategy multiplies the API load. If a user asks a simple
+question and the "Complexity Classifier" hallucinates it as "Extreme
+Complexity," the CLI could spawn multiple 32k-token requests, rapidly draining
+the user's quota or incurring massive costs [cite: 8].
+
+- **Mitigation:** Implement strict **Circuit Breakers** in `client.ts`.
+  - _Daily Limit:_ `settings.json` should support a `dailyTokenLimit`. If
+    exceeded, the CLI downgrades to "Flash" model or stops.
+  - _Concurrency Limit:_ The `reasoning.ts` module must limit parallel requests
+    based on the user's tier (e.g., Free Tier = 1 request, Paid Tier = 3
+    parallel requests) to avoid rate limiting errors (429 Too Many Requests)
+    [cite: 6].
+
+## 7. Conclusion
+
+The `gemini-cli` stands at an inflection point. Its current architecture—a
+robust, context-aware command executor—provides a solid foundation. However, to
+realize the full potential of "Agentic" workflows, it must evolve. By
+integrating **Agentic Context Engineering**, the CLI can transcend the
+limitations of static `GEMINI.md` files, becoming a system that learns from its
+own history and curates a playbook of domain mastery. Simultaneously, by
+adopting **Simple Test-Time Scaling**, the CLI can transform the "thinking"
+capabilities of Gemini 2.5/3.0 from a passive feature into an active engineering
+tool, using Budget Forcing and Best-of-N verification to deliver code that is
+not just probable, but proven.
+
+The roadmap outlined in this report—creating a Reflector/Curator loop,
+implementing adaptive Thinking Budgets, and establishing Speculative Execution
+with shell verification—provides a concrete path for the `gemini-cli` to become
+the first truly autonomous, self-improving terminal engineer. This evolution
+shifts the value proposition from "AI that helps you code" to "AI that engineers
+solutions," validating the premise that in the era of 1M+ token context windows,
+the architecture of the agent is just as critical as the intelligence of the
+model.
+
+**Sources:**
+
+1. [addyosmani.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGkvo6r6bBLqcsvjV1R0ZHMbr6YRRXWXdnwgoZwgSiuxXtf0EW91tAh9J-xKegSrrrJ4h6u4fZy6Y7iAIFPhn3gSj57CvpAH2fnBFi3K8IiehBwb8X9BoMZL-4HNg==)
+2. [arxiv.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQG23ZK24y0szJAQ6n2KbBIaTT_IPJn1pDs4CQYQJx6FGWNF1Iqmqz1epgPnErN-_-Czak0RibeYDrrU5tXnZjZbS4ligl1xAMatBYboaAtgoCvnv9XT)
+3. [Link](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEN-m23GBBntd0TuwiuFOOWRr_zIu6wx9RPpKIYKp1J24SFokv6VF5Dz84wG_OUUk-lecIJsDyNjcVq_avmqYSJfG1sWAiRdesVA5TS0W49AcaFnMgk9mXT)
+4. [github.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHB5htBeFDCiY1wzaRqXwt2EA0ga3ZtvX0mS1VDSnLWYh4nKQ43ZJa4WKbuHQzAMWsFAS6Ix3JVBmayIoA6_rNWe_HaOL0HBHT83aCCHkOLtLE0-FGmdHk0RujyBDlbsbI7Mkw-qIztWNTrZnRUf-f7EHRjsAMoB0uLgtZp-k2lEIbXQeU=)
+5. [arxiv.org](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHIs6jamVVwSXElwSu9fZ7kVRZlXAclE8qE6cc6fOsRS-5_X9cJqG458NhNeCsPjrUV2qpIfvEmBotVicqGfUY4-DYHFWp_vSQ7zzKVy-3xhxvnAUGM)
+6. [lobehub.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEkFhbVVxyWmdbj8r7wrLa62qDbLempnaZP0Obh7_dJ3CfVYrl4vv4Cj0sar6k4aNKer3XquJ4JfiloKk9MSvshPNantiwJos8dzB3llfYJCGTGt67sFekiN7YZx41zjWw7)
+7. [geminicli.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHn2hkST5-wyoHXlskiVrLDIZXGX1ZPVY6yuhkkYvi71zvUCsp4yCQxWAEPCmJrjOJWA_Gxo2S_gt--pKlTKhq_jgmU-g_Sg-KfhA2BCV1Pi1xDMWKOLoR0u1FC2KP7pIF5nXAe8a2DS4-a)
+8. [googleblog.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGMyqYhGYbATyGASBdVzRt3psR1bhsl65xatTUMuWr4qOeiE5hYXKqeYdIdu1LGoyXlEL0ZuB-F5FIAlwiRXTjAYoTj_C8vbNmS7Qa3SWAHoBbOfRskfto67NCnJWMAozLM5O_sYz9wofe5yrl2FtLbrKhZCjKJl0i0goafHbk=)
+9. [softwaresecretweapons.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFr9BT9v5LiVgGeYrLXONBABwvoWWo6fJUdWxu77ZGmr_lH3VMAiSQZ6JfODWI5zokeqr85yEweJmZMjiDdxCaeyene2q0UsVt8P_42wKG5sFG7wflF94mSTrYZ9cFFv0cIFoJKullymDrVey6TI0ANLFLSEJUFzLH4BsCgxvZ9vJ9FYrl87Jpr-SWMw3i2m8mYBQ==)
+10. [medium.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQH93_PFrCJdEhpd92srlmXt70VoKanMxMBFbGEG7IRRKKryXr80KynLRWGJkqckQ8fw7CjsZQN2xYEjhnOEOpiW_4FcVroho9j2ExUnCIzD6nhKCUqqVxKW10sIGU6A3UjcPbHYVbTTZhliQaU_wDOGrDQiN2etp-iKK2ji3zP1GhqLq5T5tvowp9_d313EGXr815SYz78BaBzeWkDJ3K9XSo76qFfn0V1Jdwj12pliK566-fzl4eKv)
+11. [medium.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFdgm58_J2WABvoCtmR6vbMUCqlhMEpA-hz8zlKN7rJGhOoqkCsj6wnbp7kR6u4mLXT_BJIPR6IAWv2KubEv3mt_mHvcrRPu1FSYrr66OVMjUSyod8TbVaK9o7FerzN6n3dUQWaKxyPR1CRMsH4GjWPgB5aBko5ajGOGPccWWyUOMnoqGbNQDpLj0CRMaSuYrgb6gnpyxpXGuNmLoWGBVtvMHeKMmmOLldBIu9hv3I8qv1R-FouZ28zb1fQJLot)
+12. [substack.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGIXE6WLz1k6NT8PBT5YILAmAVuYvhA-RIxw2M7cuFj237xG6KVskDlzpdJBuwArX0vIP2kPGfof1S6lbCfpOynFcW7QNdFd3sbc2mKtA49tTplYHwsiy1vSyVRRkmduZPLJyQfvSC2FMbXY45Mz-MAaT3omSxIwA==)
+13. [kdjingpai.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGvSGkxyQctdls-hlusZ8uLh6_UHO6j-dAR2cPEUU6Sw3611KKp45U0NNAiLw_gXF-ypwv_FGSqOpnhBVBb7T_MK98dhLTzI-apXBxnYTPfRSifqa3WAOkfqohBwY6aqA9lPo15)
+14. [github.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGtrZl4efWv_MQO0pDH7wlwCtjABxDDH2dSAoEKIsIjEEjp6bTIAS1Hq8eirkHcUMXA_nx7owBrpHPBz04lcK84C0P9gwU4Aq6ojInlThdfJFJyKnwYDVgC8uSESvPR0CPJ_hZ4pqtRd5ol3NdRMXyx0R5z2QZtP0jVaKyf7jkZBgFaUmYcxaJQTRyg0g==)
+15. [softwaresecretweapons.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQF0-gC_bA7NQOeZttlWtKLvenIBtMEJXtPaQQFCAkxDxxt034nzFkQu9gJ-y1sW-eYPKjgSiUnMCU2JYb9TKWXKQ7LAuUdTWFWXfIC1_XwMuE-v4dAJmDycOAEWbhPEIkA1neiFsG-PU25kLiH381Yr_qcHUoYlzgpelxnNGq0-5g0AVeQne42O1dKW9oEW1XZfBzPF)
+16. [medium.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE_HBlwBpTklOsw9gwI85I6zr2boa2o9Sy6oUbYfk6Vow5QeFDUwj6_vknKhvhTjJRl7qoveLynJWiOrA-VqGYqLhnC6NZj4aC-Za1v-q2JJu3zu7djM4toGCLhlXPwE5xP9bnDDdQ65uL6OLupf6dHfHeWunmliyZwEeyJMqpRKZPerauo-G6vZkMD6vdiWnq0pZb61fFqeg==)
+17. [medium.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQF_pW79tBZ3mRQkPXtlwAVNHvFpPiBdpPQisQ9eLyWnZUDntLK_dwSUdvY3kEZmfci0OKx1DKmjcGAMFwEZjRDUWMz_QtMLVwfOLS0NsVcOHS465YZUR_ncdDfQ4IOsKNQH3a6hPfbVaJriF_HUNsJZCryNQna7ujW36cYkrj6Ci-9jQ5d3ZsVpkFmugpIV6T7X4PmiNnuafDC1gRtdRY1fOan3pw==)
+18. [github.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHy48oCEUbnsrHjPTYhUbbdQmIVI7kpYj5UtvPlbvc1Pnp4RQDPyv8MvTbJVosjEuNkIlDj8wbZmSPkk8e1bypRimqpN2qCMJxX6uSPuQNvg3S8YEl6yHd1GY-GseIezTRkiva2gmPJ2Qg6GPZp)
+19. [github.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFDUi7MowuhCeA-bq4MMweGcMusWZLCNSwhGZe0pVtEFPys92XNY1tgSw5Bq4auQvdld63TEwbmtQGpsFfOrvnEoYddDM5hT9u74UqELY5CXqZyCHSrMhEgBZSc1W3Xfe8UC02G96OWQ4gKDoO1)
+20. [google.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFOAYPJhniXFx497jv0qK_wSe2M0_DAeCBn41OwrZtKEcr_io81B8N8rVLW8DEQTu7Im_bf5Lm7OiJGseGFZBzk6WKVTpEZJIp1mCNA9u-HDrJfeh1IZbaTPtPQvptzt9RlcdQOI5Q7ArXZFQ==)
+21. [trukhin.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQH8fnbHR1ti_DuWnsRX707eqedgfux9OSNpV9tATYHCSFECw4lzMBzhmoyhj5BeL5FpoFztebZpOL5BwF-XdBpJ-nKG99G0PSzqsUkimQWrqfzcEMvdaTWXG01zqRLp0VL72un2gXzFSy4WeoSt0nFMmQqq3FGVMo93__9m2C5Jk1k=)
+22. [github.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFabXU-JpVDYp54o1EAvYI9G2wDP1xV4lqlGqvqU6Ubrd7fjqEJf1V2DoDybQq1ARpUbnR5vSO59vRPe_8yyhT3PRCUP6ZflEPDr7nMoYxDtbD5quzCMCL4b4i3K4cEiviK2Wkm1yeU0hOpWSWK)
+23. [geminicli.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHaWCnhnMbtF5kidg9wj2-P8mC23ZWMmDorb2RbFzKNM3zuDK3h0hHAWVtna4G67v_Vnb8SFDVBB5mVf7UXf5_hJ7QnMonsrYdOvre0jpIEmG6PfNSxCpbO09bSjk3rfwcHxK8AK962Xw==)
+24. [google.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQGreeDFOv6k1yBeUQ803zFXVITL7kbp78IFwY2u5ilvNt-FoYCCKU_ew1T_9fMuNhiDbDTxE_PE7aTRlRlp_WV-KpiFnmeVW1Ec0v6xrUYBGgfAH2qawQR5yZeDP-oxtPQ1u4oaksX7TdMG-PFY4wM=)
+25. [researchgate.net](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFOy5wmOT__BR8yyplrn8Joh6tsGTZ1NTqPCOcF03Sa2R8IP8-Mnz9aX39dgMTzCQNPpPGAiqTXl1WjYlsu2GBIVfet4ZzBHc09msX6G0lzYghteiyx_Q9pBG_kW6V8cWG-6_FkQGHIasQhUxgy-HawMWdCgLBV3cJZgmq2jOaHxyNecy1sWR6YxmY7I03aVOCexqli9x9pPcZSh7EPmSvaabU-FPasJnhrr_cTQkA=)
+26. [huggingface.co](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFMN2wx5C3WBLhi4fkquEM8135KNfj-3Eqh0YTYtPVUH3rWxDpRAgAgJ0nctfbJYGMNsatjzR9U-9P70INp85TiDrEgexD16DmKXF_OdZLa4F2ZgQ6gXiXVm_FonBM5DgE0tsKGC7UZg_x5-K9l_2em7zuN1nhQUott4LEOYA==)
+27. [huggingface.co](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEp6MdvApWhm6R4Z0VH9ccqynNeXJ3Rc1mOyQiOvlI3X8hYKO2DlberMiLnByd6PvrE4HwTgT0QbtpQxcNo-UPnZ9SbGkbgvQRh5MMLhIgcnPVTcmbKHuwOmZGkTWrfhnUIu07y06gmXlOJ5WTh-LL5wMcJ)
+28. [lilys.ai](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQEysR67uGFdFQScgYb6mmaI2tUYHhp-1h8PrykIJ9mIRdYYGLz54e8acoDhxRT4MdQ_aCCwt_SgC9J70mZYlX28MPTk_BO7G4u0MYwN2AuoapI-kTTy2GFbC7hDcPJw5BGBt_7PJt-xgEGWBCbAAOhgRhHuV_rAZolKcKyS3xw3gAs=)
+29. [github.com](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQFiwkn8HHyJqg82KKwX9IPio1U9xK5QMwiWNhAfwsoBmFNSBndrWBXM1-G_qjfZx4jEdJlm_olIL6hm3obbk9ARHAWzozsggCdr71EAC1IFmpQ5ZA471DQUxdVYOawXHKHaaEQkVdYXG6ZObAw=)
+30. [stepsecurity.io](https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQHOKhZ1N6caVrlf_0b-t8vjsZOksmqdgU06kZnb4vYKdI6lS7FnUIJHUXfeQVfGtog553qy7Ke3NmUPkfeRQ5w9eQk6lyxzbGm3_89j8JFTiBzPFAjfmgGFZGHwZxLfbcb7dMn3jHKGEjFSmDuUkycnzKQnficUb7cU4yXHUptsAPwxR5dJ8NL7ntyHrOBUbtBq)

package-lock.json

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         "@vitest/expect": "3.2.4",
@@ -18039,6 +18060,7 @@
       "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.76.tgz",
       "integrity": "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==",
       "license": "MIT",
+      "peer": true,
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"
       }
@@ -18335,6 +18357,7 @@
       "resolved": "https://registry.npmjs.org/picomatch/-/picomatch-4.0.3.tgz",
       "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
       "license": "MIT",
+      "peer": true,
       "engines": {
         "node": ">=12"
       },

packages/a2a-server/src/agent/task.ts

@@ -24,6 +24,7 @@ import {
   type ServerGeminiErrorEvent,
   type ServerGeminiStreamEvent,
   type ToolCallConfirmationDetails,
+  type SerializableConfirmationDetails,
   type Config,
   type UserTierId,
   type AnsiOutput,
@@ -65,7 +66,10 @@ export class Task {
   scheduler: CoreToolScheduler;
   config: Config;
   geminiClient: GeminiClient;
-  pendingToolConfirmationDetails: Map<string, ToolCallConfirmationDetails>;
+  pendingToolConfirmationDetails: Map<
+    string,
+    ToolCallConfirmationDetails | SerializableConfirmationDetails
+  >;
   taskState: TaskState;
   eventBus?: ExecutionEventBus;
   completedToolCalls: CompletedToolCall[];
@@ -411,10 +415,12 @@ export class Task {
       );
       toolCalls.forEach((tc: ToolCall) => {
         if (tc.status === 'awaiting_approval' && tc.confirmationDetails) {
+          if ('onConfirm' in tc.confirmationDetails) {
             // eslint-disable-next-line @typescript-eslint/no-floating-promises
-          (tc.confirmationDetails).onConfirm(
+            tc.confirmationDetails.onConfirm(
               ToolConfirmationOutcome.ProceedOnce,
             );
+          }
           this.pendingToolConfirmationDetails.delete(tc.request.callId);
         }
       });
@@ -803,6 +809,13 @@ export class Task {
         // This will trigger the scheduler to continue or cancel the specific tool.
         // The scheduler's onToolCallsUpdate will then reflect the new state (e.g., executing or cancelled).
 
+        if (!('onConfirm' in confirmationDetails)) {
+          logger.error(
+            `[Task] Serializable confirmation details not supported yet in a2a-server for callId: ${callId}`,
+          );
+          return false;
+        }
+
         // If `edit` tool call, pass updated payload if presesent
         if (confirmationDetails.type === 'edit') {
           const payload = part.data['newContent']

packages/cli/src/config/settingsSchema.ts

@@ -1552,6 +1552,82 @@ const SETTINGS_SCHEMA = {
         description: 'Enable planning features (Plan Mode and tools).',
         showInDialog: true,
       },
+      codebaseInvestigatorSettings: {
+        type: 'object',
+        label: 'Codebase Investigator Settings',
+        category: 'Experimental',
+        requiresRestart: true,
+        default: {},
+        description: 'Configuration for Codebase Investigator subagent.',
+        showInDialog: false,
+        properties: {
+          enabled: {
+            type: 'boolean',
+            label: 'Enable Codebase Investigator',
+            category: 'Experimental',
+            requiresRestart: true,
+            default: true,
+            description: 'Enable the Codebase Investigator subagent.',
+            showInDialog: true,
+          },
+          maxNumTurns: {
+            type: 'number',
+            label: 'Max Turns',
+            category: 'Experimental',
+            requiresRestart: true,
+            default: 10,
+            description: 'Maximum number of conversational turns.',
+            showInDialog: true,
+          },
+          maxTimeMinutes: {
+            type: 'number',
+            label: 'Max Time (Minutes)',
+            category: 'Experimental',
+            requiresRestart: true,
+            default: 3,
+            description: 'Maximum execution time in minutes.',
+            showInDialog: true,
+          },
+          thinkingBudget: {
+            type: 'number',
+            label: 'Thinking Budget',
+            category: 'Experimental',
+            requiresRestart: true,
+            default: 8192,
+            description: 'The thinking budget for the model.',
+            showInDialog: true,
+          },
+          model: {
+            type: 'string',
+            label: 'Model',
+            category: 'Experimental',
+            requiresRestart: true,
+            default: undefined as string | undefined,
+            description: 'The model to use for the subagent.',
+            showInDialog: true,
+          },
+        },
+      },
+      introspectionAgentSettings: {
+        type: 'object',
+        label: 'Introspection Agent Settings',
+        category: 'Experimental',
+        requiresRestart: true,
+        default: {},
+        description: 'Configuration for Introspection Agent.',
+        showInDialog: false,
+        properties: {
+          enabled: {
+            type: 'boolean',
+            label: 'Enable Introspection Agent',
+            category: 'Experimental',
+            requiresRestart: true,
+            default: false,
+            description: 'Enable the Introspection Agent.',
+            showInDialog: true,
+          },
+        },
+      },
       adaptiveThinking: {
         type: 'object',
         label: 'Adaptive Thinking Settings',

packages/core/src/config/config.ts

@@ -56,6 +56,7 @@ import {
   DEFAULT_GEMINI_MODEL_AUTO,
   isPreviewModel,
   PREVIEW_GEMINI_MODEL,
+  DEFAULT_THINKING_MODE,
 } from './models.js';
 import { shouldAttemptBrowserLaunch } from '../utils/browser.js';
 import type { MCPOAuthConfig } from '../mcp/oauth-provider.js';
@@ -187,6 +188,18 @@ export interface AgentSettings {
   overrides?: Record<string, AgentOverride>;
 }
 
+export interface CodebaseInvestigatorSettings {
+  enabled?: boolean;
+  maxNumTurns?: number;
+  maxTimeMinutes?: number;
+  thinkingBudget?: number;
+  model?: string;
+}
+
+export interface IntrospectionAgentSettings {
+  enabled?: boolean;
+}
+
 export interface CustomTheme {
   type: 'custom';
   name: string;
@@ -1840,6 +1853,14 @@ export class Config {
     return this.agents;
   }
 
+  getCodebaseInvestigatorSettings(): CodebaseInvestigatorSettings {
+    return this.codebaseInvestigatorSettings;
+  }
+
+  getIntrospectionAgentSettings(): IntrospectionAgentSettings {
+    return this.introspectionAgentSettings;
+  }
+
   isBrowserLaunchSuppressed(): boolean {
     return this.getNoBrowser() || !shouldAttemptBrowserLaunch();
   }