PitCrew, AWS Deploy Formal Logic Solvers to Stop AI Agent Errors

Zero Hallucination Stack: Architecture for AI You Can Trust With PitCrew Agents — Ai4

Ai4

Enterprise AI adoption stalls without mathematical verification and hard-bounded tool environments because prompt tuning cannot eliminate hallucinations in regulated workflows.

AI-assisted summary of Zero Hallucination Stack: Architecture for AI You Can Trust With PitCrew Agents — original source. Check the original for full context.

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## Background Published by Ai4 on YouTube on August 19, 2026, this presentation features Sameer Goel, co-founder of PitCrew Agents. The session outlines a multi-layer architecture combining model reasoning with formal verification, firm-specific ground-truth policies, and an end-to-end evidence chain to reliably deploy AI agents in regulated enterprise environments like financial services.

## TL;DR Sameer Goel introduces PitCrew's developer platform for building autonomous enterprise AI agents that are verifiable by design. To eliminate hallucinations and ambiguity, the architecture pairs foundation models on AWS Bedrock with formal verification and mathematical solvers that translate natural language and business rules into deterministic logic. Agents are assembled from pre-packaged verifiable skills and hard-bounded to authorized enterprise APIs, preventing unauthorized tool invention. The platform also incorporates granular observability consoles, conversational audit trails, and automated static policy conflict detection across various business functions.

## Atmosphere & style The presentation functions as a focused, fast-paced technical briefing aimed at enterprise practitioners navigating the operational risks of generative AI. Goyal speaks with crisp, authoritative clarity, cutting through standard industry hype to emphasize mathematical proofs, deterministic guarantees, and production realities. The delivery feels pragmatic and solution-oriented, methodically establishing confidence through clear architectural diagrams and concrete governance frameworks.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) PitCrew develops an agentic development platform designed to make AI agents verifiable by design for high-stakes and regulated enterprise environments. - [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) In critical domains like finance, healthcare, and logistics, assessing agent outputs requires rigorous domain evidence rather than probabilistic guesswork. - [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) Natural language ambiguity frequently leads to conflicting interpretations of truth among different enterprise stakeholders. - [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) PitCrew integrates automated reasoning, formal verification, and mathematical solvers built with Amazon to ground agent logic in mathematical truth. - [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) Formal structural verification guarantees total policy coverage and ensures deterministic, auditable results whenever evaluations are re-run. - [02:37](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=157s) Enterprise workflows are modularized into pre-packaged 'verifiable skills' for common tasks like data extraction, onboarding, and policy enforcement. - [02:37](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=157s) The platform utilizes multi-turn clarifying questions upfront to extract precise developer intent and create airtight operational specifications. - [02:37](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=157s) Hallucinated tool usage is prevented by strictly binding agent execution to the customer's verified APIs and existing IT infrastructure. - [02:37](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=157s) The system runs natively on AWS Bedrock foundation models infused with environment-specific memory and context. - [04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) An operational management console provides granular telemetry and failure-mode diagnostics to feed performance corrections back into agents. - [04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) Conversational audit trails allow operators to inspect step-by-step evidence chains detailing how inputs were transformed into final answers. - [04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) The reasoning engine acts as a static policy auditor, detecting contradictions, ambiguities, and rule overlaps across enterprise documentation before deployment.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) Verifiable AI Agents for Regulated Industries

Sameer Goyal introduces PitCrew as an agentic development platform that serves a role analogous to developer tools like Cursor or Lovable, but distinguished by building AI agents that are "verifiable by design." In enterprise and mission-critical domains, the central operational risk is whether an autonomous agent is consistently making the correct decisions. This challenge becomes particularly acute in specialized sectors such as shipping, logistics, and e-commerce, as well as heavily regulated industries like healthcare and financial services. In these environments, processing a user query requires deep domain-specific knowledge, and evaluating the agent's output demands rigorous evidence and expertise rather than probabilistic guesswork.

```mermaid flowchart TD accTitle: PitCrew Verifiable Agent Architecture subgraph InputStage["1. Specification & Context"] A["User Query / Intent"] --> B["Multi-Turn Clarifying Questions"] B --> C["Enterprise Context & Tools"] end subgraph VerificationStage["2. Formal Reasoning Engine"] C --> D["Automated Reasoning Verifier"] D --> E["Mathematical Solvers"] E --> F["Functional & Structural Checks"] end subgraph ExecutionStage["3. Enterprise Deployment"] F --> G["AWS Bedrock Execution"] G --> H["Auditable & Traceable Output"] end ```

[00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) Automated Reasoning and Formal Verification Solvers

To overcome the inherent ambiguity of natural language, PitCrew built a dedicated verifier in collaboration with Amazon that relies on automated reasoning, formal verification, and mathematical solvers. Goyal points out that natural language leaves room for conflicting interpretations; for instance, in contract management, the definition of "truth" often differs fundamentally between a vendor and a customer. By utilizing automated reasoning checks, the system establishes a mathematically grounded baseline so that all parties operate against the exact same version of the truth.

Converting conversational language and enterprise business rules into formal logic requires more than standard functional testing—it demands structural verification. PitCrew deploys automated solvers to mathematically prove comprehensive rule coverage, high execution speed, and absolute accuracy. This grounding transforms agent outputs from unpredictable generations into results that are consistently auditable, objective, and deterministic whenever re-evaluated.

- **Perspective-Grounded Truth**: Resolves discrepancies across stakeholder viewpoints (e.g., vendor vs. customer contract interpretation) through formal logic. - **Structural & Functional Verification**: Uses mathematical solvers to guarantee total policy coverage and eliminate ambiguity. - **Deterministic Reliability**: Outputs remain identical and auditable across repeated evaluations.

[02:37](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=157s) Verifiable Skills, Environment Binding, and AWS Bedrock

PitCrew abstracts core day-to-day enterprise operations into a library of "verifiable skills." These include common operational tasks such as ingesting customer data during onboarding, extracting intelligence from disparate systems, and enforcing multi-layered expense, HR, or travel policies. When developers assemble agents using these pre-verified skills as guardrails, the resulting systems operate with strict objectivity.

The platform’s harness captures developer intent through multi-turn interactions, asking extensive clarifying questions upfront to generate a comprehensive, airtight initial specification. Tool execution is explicitly constrained: agents are bound directly to the customer's existing environment, APIs, and integrations. By hard-bounding the tool space to existing organizational infrastructure, the agent is prevented from generating hallucinated external calls. The platform is built natively on AWS Bedrock, running enterprise-grade foundation models capable of powering internal workforce tools and external customer-facing applications.

- **Pre-Packaged Verifiable Skills**: Standardized components for onboarding ingestion, data extraction, and policy enforcement. - **Bounded Tool Execution**: Agents are strictly restricted to authorized enterprise APIs, preventing unauthorized external tool invention. - **AWS Bedrock Foundation**: Leverages enterprise-grade foundation models with integrated environment-specific context and memory.

[04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) Observability, Auditing, and Cross-Functional Deployment

Once agents are built, PitCrew provides an operational console for scheduling, triggering, and monitoring deployments in production. The management interface captures granular traces and evaluation metrics across all runs, allowing operators to isolate exact points of failure, understand root causes, and feed diagnostic errors back into the agent to iterate on its performance. Users can also utilize a conversational natural language interface to inspect the evidence chain, reviewing how raw inputs were "transmuted into the outputs" step by step.

Beyond automating workflows, the platform functions as an automated policy auditor. Organizations can feed in comprehensive rulebooks—such as company-wide HR, expense, or travel policies—and the reasoning engine will analyze the entire rule set to detect contradictions, ambiguities, or overlapping clauses before deployment. Goyal emphasizes that these verifiable patterns apply broadly across enterprise functions, including supply chain, CRM, procurement, legal, and sales, inviting attendees to Booth 514 to review live customer deployments.

- **Execution Telemetry**: Granular monitoring consoles with traces, failure mode diagnostics, and feedback loops for agent refinement. - **Conversational Audit Trails**: Natural language drill-downs that verify the intermediate logic between raw inputs and final answers. - **Policy Conflict Detection**: Static analysis of enterprise policy documents to catch contradictory rules and logic gaps. - **Broad Functional Reach**: Cross-departmental applicability spanning procurement, legal, sales, CRM, and supply chain logistics.

## Notable quotes > Most enterprise AI stalls at the same wall - the model hallucinates, fabricates citations, and gets the math wrong. Better prompts won't solve this but better architecture will. — Sameer Goel

> combine Model's reasoning with formal verification of agentic work, firm-specific ground truth policies that constrain what the agent can do, and an evidence chain that traces every output to its source data. — Sameer Goel

> Not theory but production systems running daily across thousands of accounts. — Sameer Goel

> [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) verifiable by design — Sameer Goyal

> [04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) transmuted into the outputs — Sameer Goyal

## Mentioned Sameer Goel, Sameer Goyal, PitCrew, PitCrew Agents, Cursor, Lovable, Amazon, AWS Bedrock

## Video description Sameer Goel - Co-founder, PitCrew Agents

Most enterprise AI stalls at the same wall - the model hallucinates, fabricates citations, and gets the math wrong. Better prompts won't solve this but better architecture will. We will walk through the multi-layer stack we built at PitCrew to deploy AI agents in regulated financial services, where an incorrect number has legal consequences. Specifically, combine Model's reasoning with formal verification of agentic work, firm-specific ground truth policies that constrain what the agent can do, and an evidence chain that traces every output to its source data. Attendees will leave with a concrete architecture pattern they can apply to any domain where AI accuracy isn't optional. Not theory but production systems running daily across thousands of accounts.

[Source](https://www.youtube.com/watch?v=R1A5cdPM9Uo)

中文简报

PitCrew携手AWS部署形式逻辑求解器以遏制AI Agent错误 由于提示词调优无法消除受监管工作流中的幻觉，若缺乏数学验证和具备硬边界的工具环境，企业级AI的采用将陷入停滞。 Sameer Goel介绍了PitCrew用于构建“设计即具可验证性”的自主企业级AI智能体开发者平台。为消除幻觉与歧义，该架构将AWS Bedrock上的基础模型与形式化验证及数学求解器相结合，把自然语言和业务规则转化为确定性逻辑。智能体由预封装的可验证技能组装而成，并严格受限于已获授权的企业API，以防止生成未授权的工具。此外，该平台还整合了细粒度的可观测性控制台、对话审计追踪，以及覆盖各项业务职能的自动化静态策略冲突检测。 PitCrew 推出面向金融、医疗及物流等受监管行业的 Agent 开发平台，采用“设计即验证”架构，以严密领域证据替代概率性猜测。 (0:04) 平台与 Amazon 合作构建自动推理验证器与数学求解器，对业务规则开展形式化结构验证，确保评估结果具有确定性与可审计性。 (0:55) 基于 AWS Bedrock 构建，PitCrew 提供开箱即用的“可验证技能”模块，并将 Agent 严格绑定至企业现有 API 与系统环境，彻底杜绝幻觉工具调用。 (2:37) 系统配备执行遥测与自然语言证据链审查控制台，并支持对 HR、差旅等企业政策进行静态分析，在部署前自动检测条款冲突与逻辑漏洞。 (4:21)

Reading summary

Sameer Goyal presents PitCrew as an enterprise development platform that replaces probabilistic guesswork with formal verification, enabling autonomous AI agents to operate deterministically in regulated and mission-critical industries. Mathematical Solvers for Eliminating Linguistic Ambiguity (0:55) In high-stakes sectors like logistics, financial services, and healthcare, natural language leaves broad room for conflicting interpretations between stakeholders, such as differing definitions of contract terms between vendors and customers. To counter this, PitCrew developed an automated reasoning engine in collaboration with Amazon that translates conversational requests and enterprise guidelines into formal logic. Rather than relying purely on functional testing, the architecture applies mathematical solvers to execute structural verification across rules. Goyal states that this mathematical grounding guarantees total policy coverage, yielding deterministic, repeatable results that maintain identical outputs across re-evaluations. Bounded Execution via Reusable Verifiable Skills (2:37) To prevent agents from hallucinating unauthorized external API calls, PitCrew hard-bounds agent execution exclusively to existing customer infrastructure, tools, and enterprise interfaces. The platform engages users in multi-turn clarifying conversations prior to execution, creating an unambiguous specification before invoking tools. Operational workflows are assembled from a pre-packaged library of verified components designed for common enterprise functions, including customer onboarding, cross-system data extraction, and expense policy enforcement. PitCrew runs natively on AWS Bedrock to integrate enterprise foundation models directly with local context and memory. Continuous Telemetry and Static Policy Auditing (4:21) For live operations, the platform supplies an observability console that records granular execution traces and diagnostics to isolate failures and feed corrections back into agent configurations. A conversational natural language interface allows compliance teams to inspect the underlying evidence chain and verify step-by-step how raw enterprise inputs were converted into specific agent outputs. In addition to runtime management, the reasoning engine functions as an automated static policy auditor. Organizations can ingest complex rule sets—including company travel, HR, and procurement guidelines—allowing the system to detect contradictory clauses, coverage gaps, and ambiguous definitions before an agent is deployed into production. PitCrew argues that formal verification solves the trust barrier for autonomous agents in regulated sectors, though the framework's reliability depends on upfront specification clarity, hard API constraints, and the static resolution of enterprise policy conflicts.

精炼阅读摘要

PitCrew 联合创始人 Sameer Goyal 提出，在受监管与关键业务场景中，自主智能体必须具备“设计即验证”（verifiable by design）的特性；该平台通过结合形式化验证、数学求解器与受限的企业执行环境，力求消除概率性猜测带来的幻觉，实现可审计、确定性的企业决策支持。 基于形式化验证消除语义歧义与分歧 (0:55) Goyal 指出，自然语言的内在模糊性常导致不同利益主体对业务规则产生理解分歧，例如合同管理中客户与供应商对“事实”的定义可能截然不同。为确立一致的标准，PitCrew 与 Amazon 合作构建了结合自动化推理与数学求解器的专用验证器，通过形式化逻辑建立数学基准，确保各方在统一的事实版本下运行。 该方案超越了传统的常规功能测试，转而引入结构化验证，利用求解器在数学层面证明策略规则的全覆盖性、执行速度与绝对准确度。这种机制将大语言模型不可预测的生成过程转化为客观、可重复评估且结果一致的确定性输出，满足了高度合规行业对审查与定责的要求。 模块化预验证技能与严格工具边界限制 (2:37) PitCrew 将企业日常核心操作抽象为标准化的“可验证技能”库，覆盖客户入职数据接入、跨系统信息抽取以及多层级报销、差旅和人事政策的执行。开发者可将这些经过预验证的模块作为刚性护栏组装智能体，确保业务流程依照客观规则运行。 在开发交互中，平台机制会通过多轮提问主动厘清开发者意图，以此生成完备严谨的初始规范。同时，智能体的工具调用权限被严格限定在企业既有的 API、集成设施及内部环境中，杜绝了模型自主编造外部未授权调用的可能性；该平台原生搭建于 AWS Bedrock 之上，利用企业级基础模型支撑内部工作流与对外应用。 执行链路遥测与静态策略冲突检测 (4:21) 在生产运营方面，PitCrew 配套提供了运维控制台，用于记录细粒度的执行追踪轨迹与评估指标，帮助管理人员精准定位故障根因，并将诊断信息反馈给系统以实现持续优化。此外，系统支持用户通过自然语言会话界面追溯证据链条，直观复核从原始输入转化为最终输出的每一个中间步骤。 除工作流自动化外，该推理引擎还具备静态规则审计功能。企业可直接导入整套 HR、费用或差旅政策手册，验证引擎会在部署前全局扫描整套规则体系，主动发现其中的条款矛盾、歧义与重叠漏洞。Goyal 表示，此类验证模式具备通用性，可广泛适配于供应链管理、客户关系管理（CRM）、采购、法律及销售等多个业务职能。 PitCrew 提出的架构主张通过形式化证明与环境受限来根除模型幻觉，但这一体系高度依赖业务规则向形式逻辑的精准转换以及对现有企业 API 的深度绑定；其应对非结构化或高度动态业务场景的普适性，仍需在更多复杂垂直行业的规模化部署中持续检验。

Detailed notes — zh

## Background 该内容由行业峰会频道 Ai4 于 2026 年 8 月 19 日在 YouTube 平台发布。视频由 PitCrew Agents 联合创始人 Sameer Goel 主讲，围绕企业级 AI 在幻觉与合规方面的瓶颈，介绍了在金融等强监管场景中通过形式化验证、企业基准策略与证据链实现“零幻觉”的智能体架构。 ## TL;DR Sameer Goel 介绍了专注于高风险垂直领域的“天生可验证”（verifiable by design）AI 智能体开发平台 PitCrew。该系统在任务初期通过主动多轮澄清提问生成完备规范，并深度集成亚马逊自动推理技术（Automated Reasoning）与数学求解器，从结构和功能两个维度对业务逻辑进行形式化验证。整个平台运行在 AWS Bedrock 之上，严格复用企业现有 API 与工具，同时通过控制台提供全链路链路追踪（Traces）、失败归因与审计能力，有效落地于合同解析、差旅报销与跨部门策略审计等严苛业务场景。 ## Atmosphere & style 演讲整体呈现出干练务实、直击痛点的技术布道风格，没有宽泛的宏大叙事，而是直面大模型胡言乱语与法律合规风险的现实难题。讲者语调沉稳且自信，以严密的工程逻辑层层推导，将数学级验证与实际企业落地无缝串联。整场分享节奏紧凑专业，让受众在聆听过程中真切感受到一种由严谨工程架构带来的踏实感与确定性。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) PitCrew 致力于打造“天生可验证”（verifiable by design）的智能体开发平台，解决企业级 AI 在高风险垂直场景下的决策可信度痛点。 - [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) 在金融与医疗等强监管行业，模型幻觉与计算偏差伴随严重的业务与法律风险，单纯依靠优化 Prompt 无法根治，必须从底层架构切入。 - [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) 联合亚马逊引入自动推理（Automated Reasoning）与形式化验证数学求解器，在数学逻辑层面对智能体输出进行严格验证。 - [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) 针对自然语言的模糊性与歧义性，系统通过多轮澄清性提问（clarifying questions）主动消除语义分歧，沉淀高完备度的需求规范。 - [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) 引入统一的事实锚定（Grounding）机制，消除合同与协作场景中不同业务方对事实定义的偏差，确保基准事实一致。 - [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) 同时执行结构验证与功能验证，在保证模型执行效率的同时，最大化业务规则的覆盖率、完备性与客观准确性。 - [03:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=201s) Agent Harness 架构对工具调用实施强约束策略，强制复用企业现有的内部 API 与工具库，严禁智能体随意生成不受控的外联工具。 - [03:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=201s) 平台底层深度集成 AWS Bedrock 企业级模型能力，支持将高可靠智能体一键部署至面向内部员工或外部客户的生产环境。 - [04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) 控制台提供全链路追踪（Traces）与评估功能，支持端到端审查输入数据转化为最终结论的完整证据链，实现完全可审计。 - [04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) 内置策略引擎支持策略一致性审计（Policy Audits），能自动遍历并排查 HR、合同与采购规则中的条款冲突，确保跨部门协同的确定性。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) 平台定位与高风险行业的 AI 可信决策挑战 PitCrew 联合创始人 Sameer Goyal 开场介绍了公司的核心定位：PitCrew 是一个智能体（Agent）开发平台，其产品形态类似于 AI 代码生成领域的 Cursor 或 Lovable，但核心差异在于它构建的智能体在架构设计上是“天生可验证的”（verifiable by design）。 在构建和部署 AI 智能体时，企业最核心的担忧始终是“智能体是否在做出正确的决策”。这一痛点在特定垂直行业表现得尤为剧烈——无论是在电子商务、航运物流，还是在受到严格监管的金融服务与医疗保健行业。在这些场景中，解析用户的查询意图需要极度深厚的专业领域知识；而当评估模型生成的响应时，同样需要确凿的事实证据与专业判断力来断定该输出在业务和法律层面上是否真实有效。 ```mermaid flowchart TD accTitle: PitCrew 可验证智能体架构流程图 subgraph Capture["1. 意图捕获与规范构建"] A["用户自然语言意图"] --> B["多轮澄清提问"] B --> C["高完备度初始规范 Specification"] end subgraph Core["2. 运行时 Harness 与执行环境"] C --> D["Agent Harness 运行框架"] E["企业内部 API / 工具集成"] --> D F["上下文与记忆 Context/Memory"] --> D G["AWS Bedrock 企业级模型"] --> D end subgraph Verify["3. 自动推理与形式化验证"] D --> H["形式化验证与求解器 Solvers"] H --> I["规则与事实对齐 Grounding"] I --> J["结构与功能验证 Structural & Functional"] end subgraph Ops["4. 生产运维与审计闭环"] J --> K["可审计/一致性输出"] K --> L["Console 调度与监控 Traces/Evals"] L -.->|"失败归因与迭代"| C end ``` --- ### [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) 结合亚马逊自动推理的形式化验证与事实对齐 为了彻底解决输出不可靠的问题，PitCrew 携手亚马逊（Amazon），基于自动推理（Automated Reasoning）技术联合构建了一套专用验证器（Verifier）。该系统利用形式化验证（Formal Verification）和数学求解器（Solvers），在数学层面上严格检验智能体是否做出了符合逻辑与规范的正确决策。 自然语言天生具有模糊性和歧义性，因此在构建智能体时，系统必须注入丰富的业务上下文，并通过主动发起多轮“澄清性提问”（clarifying questions）来消除语义歧义，以此建立精确的逻辑框架。此外，“事实”（Truth）的定义在不同业务视角下往往存在偏差——例如在合同管理场景中，供应商与客户对同一条款的“事实标准”定义可能截然不同。PitCrew 通过自动推理检查将事实牢牢锚定（Grounding），确保所有参与方看到的都是同一版本、毫无歧义的基准事实。 在将自然语言和业务规则转化为机器逻辑的过程中，平台不仅执行功能验证（Functional Verification），还执行结构验证（Structural Verification）。借助数学求解器与表达式验证，系统能够在保证执行速度的同时，确保规则覆盖率（Coverage）、完备性（Comprehensiveness）与准确性（Accuracy）。这种将规则、事实与高质量意图查询深度对齐的机制，使模型输出具备了极高的可验证性、可审计性、客观性以及在反复评估中的高度一致性。 - **核心验证机制**：结合形式化方法与 SMT 求解器，从结构和功能两个维度对业务逻辑进行数学级验证。 - **内置可验证技能库**：涵盖客户入职（Onboarding）时的数据摄取、情报与洞察分析中的数据处理、跨异构源的数据提取，以及 HR、差旅、报销等各类复杂策略规则的自动化校验。 --- ### [03:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=201s) 规范驱动的 Harness 架构与 AWS Bedrock 企业级集成 PitCrew 平台的核心构建流程始于精准的意图捕获。系统通过多轮人机交互，尽可能多地抛出澄清性问题，从而在编码或执行前沉淀出一份极其详尽完备的初始需求规范（Specification）。 这份规范随后被输入到底层的 Agent Harness（智能体运行框架）中，结合精准的记忆管理、上下文工程以及工具链进行调度。在工具调用方面，PitCrew 采取了极其严密的约束策略：智能体调用的工具被强行绑定在企业现有的组织环境内，深度对接客户已有的内部 API、系统集成与既有工具库。平台严格限制智能体随意生成不合规的新工具——如果企业内部已经存在现成的 API 或系统能力，智能体就必须在受控框架内复用，严禁越界外联。 - **云端基础设施**：PitCrew 平台全面构建在 AWS Bedrock 之上，直接调度企业级基础大模型。 - **部署灵活性**：构建完成的智能体可直接无缝部署到生产环境，既支持企业内部团队协同使用，也可直接面向外部客户提供高可靠服务。 --- ### [04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) 生产环境运维监控、审计追踪与跨业务场景落地 当智能体进入生产就绪状态后，用户可以通过 PitCrew 提供的管理控制台（Console）对其进行定时调度、事件触发与全生命周期运营管理。在运维监控面板中，团队能够实时跟踪任务的成功率、识别失败节点并深度下钻失败归因，进而将错误日志转化为数据飞轮，持续反哺和优化智能体的表现。 平台提供了全链路的追踪（Traces）与评估（Evaluations）工具，并配备了对话式自然语言交互界面。用户不仅可以直观地查看输入与输出，还能以可验证的方式端到端审查输入数据是如何一步步“转化”（transmuted）为最终输出的整个推理链条。结合平台内置的策略引擎（Policy Engine）和组织上下文环境，系统在各核心业务职能中展现了极高的实用价值： - **典型业务应用场景**： - **合同与报销管理**：自动化解析并执行复杂的合同条款、差旅标准与报销审批规则。 - **策略一致性审计（Policy Audits）**：在 HR 等规则繁杂的部门，系统可遍历审查所有规章制度，自动排查条款之间是否存在逻辑冲突或语义歧义，确保员工提问时能获得准确锚定的权威解答。 - **跨部门协同**：广泛适用于供应链（Supply Chain）、客户关系管理（CRM）、采购（Procurement）、法务（Legal）及销售（Sales）等对准确度要求极高的业务线。 演讲最后，Sameer Goyal 邀请现场参会者前往 514 号展位观看真实生产环境下的系统实操演示，并开放了现场问答交流。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) PitCrew 是一个智能体（Agent）开发平台，其产品形态类似于 AI 代码生成领域的 Cursor 或 Lovable，但核心差异在于它构建的智能体在架构设计上是“天生可验证的”（verifiable by design）。 — Sameer Goyal > [00:04](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=4s) 在构建和部署 AI 智能体时，企业最核心的担忧始终是“智能体是否在做出正确的决策”。 — Sameer Goyal > [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) PitCrew 携手亚马逊（Amazon），基于自动推理（Automated Reasoning）技术联合构建了一套专用验证器（Verifier）。 — Sameer Goyal > [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) 该系统利用形式化验证（Formal Verification）和数学求解器（Solvers），在数学层面上严格检验智能体是否做出了符合逻辑与规范的正确决策。 — Sameer Goyal > [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) 通过主动发起多轮“澄清性提问”（clarifying questions）来消除语义歧义，以此建立精确的逻辑框架。 — Sameer Goyal > [00:55](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=55s) PitCrew 通过自动推理检查将事实牢牢锚定（Grounding），确保所有参与方看到的都是同一版本、毫无歧义的基准事实。 — Sameer Goyal > [03:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=201s) 系统通过多轮人机交互，尽可能多地抛出澄清性问题，从而在编码或执行前沉淀出一份极其详尽完备的初始需求规范（Specification）。 — Sameer Goyal > [03:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=201s) 智能体调用的工具被强行绑定在企业现有的组织环境内，深度对接客户已有的内部 API、系统集成与既有工具库。平台严格限制智能体随意生成不合规的新工具 — Sameer Goyal > [04:21](https://www.youtube.com/watch?v=R1A5cdPM9Uo&t=261s) 用户不仅可以直观地查看输入与输出，还能以可验证的方式端到端审查输入数据是如何一步步“转化”（transmuted）为最终输出的整个推理链条。 — Sameer Goyal ## Mentioned Sameer Goyal, PitCrew, PitCrew Agents, Amazon, AWS Bedrock, Cursor, Lovable, SMT 求解器, Agent Harness ## Video description Sameer Goel - Co-founder, PitCrew Agents Most enterprise AI stalls at the same wall - the model hallucinates, fabricates citations, and gets the math wrong. Better prompts won't solve this but better architecture will. We will walk through the multi-layer stack we built at PitCrew to deploy AI agents in regulated financial services, where an incorrect number has legal consequences. Specifically, combine Model's reasoning with formal verification of agentic work, firm-specific ground truth policies that constrain what the agent can do, and an evidence chain that traces every output to its source data. Attendees will leave with a concrete architecture pattern they can apply to any domain where AI accuracy isn't optional. Not theory but production systems running daily across thousands of accounts. [Source](https://www.youtube.com/watch?v=R1A5cdPM9Uo)

Source quotes

[0:04] Sameer Goyal: PitCrew 是一个智能体（Agent）开发平台，其产品形态类似于 AI 代码生成领域的 Cursor 或 Lovable，但核心差异在于它构建的智能体在架构设计上是“天生可验证的”（verifiable by design）。 [0:04] Sameer Goyal: 在构建和部署 AI 智能体时，企业最核心的担忧始终是“智能体是否在做出正确的决策”。 [0:55] Sameer Goyal: PitCrew 携手亚马逊（Amazon），基于自动推理（Automated Reasoning）技术联合构建了一套专用验证器（Verifier）。 [0:55] Sameer Goyal: 该系统利用形式化验证（Formal Verification）和数学求解器（Solvers），在数学层面上严格检验智能体是否做出了符合逻辑与规范的正确决策。 [0:55] Sameer Goyal: 通过主动发起多轮“澄清性提问”（clarifying questions）来消除语义歧义，以此建立精确的逻辑框架。 [0:55] Sameer Goyal: PitCrew 通过自动推理检查将事实牢牢锚定（Grounding），确保所有参与方看到的都是同一版本、毫无歧义的基准事实。 [3:21] Sameer Goyal: 系统通过多轮人机交互，尽可能多地抛出澄清性问题，从而在编码或执行前沉淀出一份极其详尽完备的初始需求规范（Specification）。 [3:21] Sameer Goyal: 智能体调用的工具被强行绑定在企业现有的组织环境内，深度对接客户已有的内部 API、系统集成与既有工具库。平台严格限制智能体随意生成不合规的新工具 [4:21] Sameer Goyal: 用户不仅可以直观地查看输入与输出，还能以可验证的方式端到端审查输入数据是如何一步步“转化”（transmuted）为最终输出的整个推理链条。

Source quotes

Sameer Goel: Most enterprise AI stalls at the same wall - the model hallucinates, fabricates citations, and gets the math wrong. Better prompts won't solve this but better architecture will. Sameer Goel: combine Model's reasoning with formal verification of agentic work, firm-specific ground truth policies that constrain what the agent can do, and an evidence chain that traces every output to its source data. Sameer Goel: Not theory but production systems running daily across thousands of accounts. [0:04] Sameer Goyal: verifiable by design [4:21] Sameer Goyal: transmuted into the outputs

Original transcript

[0:04] Good morning, ladies and gentlemen. Um my name  is Sameer Goyal. I'm co-founder of Pit Crew. [0:09] We are a agentic development platform.  Uh think of us very similar to what [0:13] a cursor or a lovable is, but with  agents that are verifiable by design. [0:20] Whenever we are making agents, one of the  biggest, uh you know, uh things that we kind of [0:25] worry about is is the agent kind of  making the right decisions, right? Uh [0:30] especially in uh specialized industries like  e-commerce or uh you know, shipping and logistics, [0:35] and even regulated industries like  financial services and health care, [0:39] you need when you're doing the query or intent,  there is specialized knowledge that is needed. [0:43] When you are kind of looking at the responses,  at that time as well, you need evidence and [0:49] uh you know, uh expertise to judge whether  the response is actually valid or not. [0:55] We have built an verifier along with  Amazon uh using automated reasoning. [1:00] And what we do is we kind of use formal  verification and solvers to verify whether [1:06] the agents are making the right decisions or  not. Typically, when you're working with uh [1:13] natural language, natural language is  inherently, you know, ambiguous. So, [1:17] as you are building agents, you need to  actually add a lot of context, a lot of [1:21] clarifying questions need to be asked for it to be  able to kind of build the right framework for you. [1:29] The definition of truth also changes. Like,  in the world of uh say contract management, [1:34] the truth is different from a for a vendor versus  the truth is different for a customer, right? So, [1:40] we use automated reasoning checks to  make sure that the truth is grounded [1:44] and everybody is seeing the same side  and the same version of that truth. [1:49] Additionally, when you are kind of converting  uh natural language and rules into logic, [1:56] you need not just functional verification,  but you need structural verification. [2:00] And that kind of we use again using  solvers and mathematical expressions [2:05] to make sure that there is coverage,  there is comprehensiveness, and accuracy, [2:09] as well as speed for verifying whether the  agent is doing what it is supposed to be doing. [2:15] What that means is when there is essentially like  grounding of the rule, grounding of the truth, [2:21] as well as a much better query, the result is more  verifiable, more auditable, and you know, more [2:29] objective, as well as it becomes more consistent  in any time it is evaluated again and again. [2:37] What that means is we offer the verifier where  I'm sure you all of you guys have used these [2:43] skills in your day-to-day life, right? There is  data ingestion that you're doing when you are [2:47] onboarding customers. There is data  that you are processing, you know, [2:51] for insights and intelligence. There is data  that you are extracting from various different [2:56] sources to kind of put together those insights.  There are whether it's HR or travel or expense, [3:03] there are various different policies and,  you know, that you're applying for it. So, [3:07] we offer all of these as verifiable skills on our  platform. And within that guardrail, when you're [3:13] building agents, the the agents are much more  kind of objective in their in their operation. [3:21] Using our platform, you can capture your intent,  multi-turn, we ask as many clarifying questions [3:28] as possible so that we have the most comprehensive  initial specification that we can kind of build. [3:34] That's then used by our harness with  the right memory, context, tools, etc. [3:41] Even the tools that we have here, we  try to tie them to your environment, [3:45] so your existing integrations, your existing  APIs, your existing tools that you may have, [3:50] along with the tools that we offer, so that it is  bound within that framework and it is not kind of [3:56] trying to create tools that it should not. If that  tool already exists, if that API already exist, [4:01] why should it be going out? And as it's not just  about build, our platform is on AWS Bedrock, [4:08] you know, where we are using enterprise grade  models. You can also deploy all of these [4:12] agents in production for for use by your teams  internally or by your customers externally. [4:21] As agents are complete and available for  running, we can kind of you can trigger them, [4:27] schedule them. You know, there are there is a  console that you can use to kind of operate them. [4:33] Also, when an agent is running, monitor how  many successfully kind of completed, where were [4:38] the failures, why were the failures, use some of  those errors etc. to improve the agent back again. [4:44] You know, there are traces and evaluations that  are available and and you can kind of keep kind of [4:49] improving the agent as well. And there is natural  language interface, conversational interface, [4:55] which you can use to interact with the  inputs as well as with the outputs and see [5:00] how those inputs were trans trans you know,  transmuted into the outputs, what was the process. [5:06] All of those things are verifiable as well. Of  course, as I said, in-built skills, there is [5:12] grounding of the truth using automated reasoning.  You know, there is a policy engine that we have [5:17] that that you can kind of use and the environment  in which you are operating, which is your [5:22] your your organizational environment and  context that we are kind of building it in. [5:29] Essentially, you can use this for all of  your everyday jobs and works like, you know, [5:35] contract management, expense management, travel  leave policies, implementation of those rules [5:42] so that they are much more clearer. You can even  build policy audits where for a particular kind [5:48] of function if there is say for example an  HR policy you can look at all the rules and [5:53] ensure that there is no ambiguity no rules are  conflict conflicting with each other and whenever [5:58] somebody is asking a question they have the right  grounding to be able to answer that question right [6:03] a lot of these are all already there and across  functions right whether you are in supply chain [6:09] whether you are you know customer relationship  management procurement legal sales all of these [6:16] use cases apply everywhere uh we have a booth  514 if you want to see a demo of the product [6:23] happy to kind of show you walk you through what  what this kind of looks like in real world and [6:28] you know happy to kind of take questions  if anybody has at this point of time