{"title":"AI Engineering","description":"\u003ch1 class=\"cons-title\"\u003eAI Agent Engineering Services\u003c\/h1\u003e\n\u003cp\u003eTechwall designs, evaluates, and deploys production AI agents — from architecture and tool orchestration through eval harnesses and forward-deployed rollout. We combine agent engineering with \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e and electronics manufacturing for enterprise and AIoT programs.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eStart here:\u003c\/strong\u003e \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-harness-design\"\u003eAgent Harness \u0026amp; Eval Design\u003c\/a\u003e · \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eForward Deployed Engineering\u003c\/a\u003e\u003c\/p\u003e\n\u003cp\u003eWe support LLM agents, RAG pipelines, computer vision, and edge inference — integrated with the hardware we manufacture when your product demands it. Explore integration, generative AI, AIoT, and infrastructure services below.\u003c\/p\u003e\n","products":[{"product_id":"openclaw-development","title":"OpenClaw (Clawbot) Integration and Design","description":"\u003cp\u003eOpenClaw (Clawbot) Integration and Design delivers smart, scalable automation by creating custom multi‑agent systems tailored to your workflows, connecting Clawbot with your existing tools and APIs to cut manual work, boost efficiency, and support business growth; whether for small teams or enterprises, we design and optimize OpenClaw workflows that integrate seamlessly with platforms like N8N, Make, Zapier, and Huginn, enabling AI‑driven processes and a reliable, future‑ready automation ecosystem.\u003c\/p\u003e","brand":"Tech Wall Electronics","offers":[{"title":"Default Title","offer_id":44875503665288,"sku":null,"price":0.0,"currency_code":"HKD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0641\/6692\/0328\/files\/Openclaw_logo_image.jpg?v=1773049582"},{"product_id":"ai-agent-design","title":"AI Agent Design","description":"\u003ch2\u003eAI Agent Design \u0026amp; Architecture\u003c\/h2\u003e\u003cp\u003eMost teams can demo an agent in a notebook. Few can ship one that survives production traffic, changing APIs, and real user behavior. Techwall's \u003cstrong\u003eAI Agent Design\u003c\/strong\u003e service closes that gap: we define the architecture, tool boundaries, and operational model before a single line of integration code is written.\u003c\/p\u003e\u003cp\u003eWe work with product leaders, CTOs, and innovation teams who need agents that connect to real systems — CRMs, ERPs, factory MES, device fleets, and custom APIs — not chatbots that stop at the browser.\u003c\/p\u003e\u003ch3\u003eWhat We Design\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSingle-agent workflows\u003c\/strong\u003e — focused assistants with clear scope, tool access, and escalation paths.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-agent systems\u003c\/strong\u003e — planner\/executor patterns, specialist sub-agents, and supervised handoffs.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTool \u0026amp; MCP layers\u003c\/strong\u003e — structured connectors so models invoke APIs safely and predictably.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGuardrails\u003c\/strong\u003e — input\/output filters, policy checks, approval gates, and audit trails.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePhysical AI agents\u003c\/strong\u003e — agents that bridge cloud reasoning with edge devices and manufacturing workflows (\u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI hub\u003c\/a\u003e).\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eOur Design Process\u003c\/h3\u003e\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eDiscovery\u003c\/strong\u003e — map stakeholders, data sources, latency requirements, and compliance constraints.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUse-case framing\u003c\/strong\u003e — define agent roles, success metrics, and what stays human-owned.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eArchitecture draft\u003c\/strong\u003e — diagrams for agent graph, tool registry, auth model, and deployment topology.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePrototype scope\u003c\/strong\u003e — thin vertical slice to validate tool calls and failure modes.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eProduction roadmap\u003c\/strong\u003e — phased rollout tied to \u003ca href=\"\/products\/ai-agent-harness-design\"\u003eharness \u0026amp; eval design\u003c\/a\u003e and optional \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eforward deployed engineering\u003c\/a\u003e.\u003c\/li\u003e\n\u003c\/ol\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eArchitecture decision record (ADR) and system diagrams\u003c\/li\u003e\n\u003cli\u003eTool\/MCP specification with schemas and error contracts\u003c\/li\u003e\n\u003cli\u003eGuardrail \u0026amp; security checklist\u003c\/li\u003e\n\u003cli\u003ePilot backlog with acceptance criteria\u003c\/li\u003e\n\u003cli\u003eTechnology recommendations (models, frameworks, observability) — pragmatic, not a laundry list\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eHow This Differs From Integration\u003c\/h3\u003e\u003cp\u003eOur \u003ca href=\"\/products\/ai-agent-enterprise-integration\"\u003eAI Agent Enterprise Integration\u003c\/a\u003e service focuses on wiring agents into N8N, Make, Zapier, and existing automation stacks. \u003cstrong\u003eAgent Design\u003c\/strong\u003e is upstream: the blueprint that makes integration durable. Many clients engage Design first, then Integration or FDE for rollout.\u003c\/p\u003e\u003ch3\u003eRelated Services\u003c\/h3\u003e\u003cp\u003e\u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-harness-design\"\u003eAgent Harness \u0026amp; Eval\u003c\/a\u003e · \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eForward Deployed Engineering\u003c\/a\u003e · \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e\u003c\/p\u003e\u003ch3\u003eFrequently Asked Questions\u003c\/h3\u003e\u003cp\u003e\u003cstrong\u003eDo you build the agent or only design it?\u003c\/strong\u003e\u003cbr\u003eBoth. Design can be a standalone engagement; we often continue into build, harness design, and deployment.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhich models and frameworks do you support?\u003c\/strong\u003e\u003cbr\u003eWe are model-agnostic — OpenAI, Anthropic, open-weight models, and edge deployments. Framework choices depend on your stack; we document trade-offs in the architecture phase.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan you design agents for regulated industries?\u003c\/strong\u003e\u003cbr\u003eYes. We incorporate audit logging, data residency, and human approval steps into the design from day one.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eHow long does agent design take?\u003c\/strong\u003e\u003cbr\u003eTypical discovery + architecture: 2–4 weeks for a focused use case; larger multi-agent programs are phased.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWe already have an internal team — can you augment them?\u003c\/strong\u003e\u003cbr\u003eYes. We deliver architecture packages your team can implement, or pair via forward deployed engineering.\u003c\/p\u003e\u003cscript type=\"application\/ld+json\"\u003e{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Do you build the agent or only design it?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Both. Design can be a standalone engagement; we often continue into build, harness design, and deployment.\"}},{\"@type\":\"Question\",\"name\":\"Which models and frameworks do you support?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"We are model-agnostic — OpenAI, Anthropic, open-weight models, and edge deployments. Framework choices depend on your stack.\"}},{\"@type\":\"Question\",\"name\":\"Can you design agents for regulated industries?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. We incorporate audit logging, data residency, and human approval steps into the design from day one.\"}},{\"@type\":\"Question\",\"name\":\"How long does agent design take?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Typical discovery and architecture: 2–4 weeks for a focused use case; larger multi-agent programs are phased.\"}},{\"@type\":\"Question\",\"name\":\"Can Techwall augment our internal AI team?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. We deliver architecture packages your team can implement, or pair via forward deployed engineering.\"}}]}\u003c\/script\u003e","brand":"Tech Wall Electronics","offers":[{"title":"Default Title","offer_id":46479069446280,"sku":null,"price":0.0,"currency_code":"HKD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0641\/6692\/0328\/files\/TechwallAIAgentDevelopmentService_7ac23e6a-32f7-4fcb-b3c1-f949dfd7dab7.jpg?v=1788428273"},{"product_id":"ai-agent-harness-design","title":"Agent Harness \u0026 Eval Design","description":"\u003ch2\u003eAgent Harness \u0026amp; Evaluation Design\u003c\/h2\u003e\u003cp\u003eAgents fail differently than traditional software: non-deterministic outputs, tool-call side effects, and context drift make \"it worked in the demo\" a poor quality bar. Techwall's \u003cstrong\u003eAgent Harness \u0026amp; Eval Design\u003c\/strong\u003e service gives you a repeatable way to measure agent quality before release and after every change.\u003c\/p\u003e\u003cp\u003eWe design harnesses that fit your release process — whether you ship weekly prompts, monthly model upgrades, or continuous deployments to thousands of users.\u003c\/p\u003e\u003ch3\u003eWhy Eval Harnesses Matter\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRegression safety\u003c\/strong\u003e — catch broken tool schemas and policy violations before users do.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eModel migration\u003c\/strong\u003e — compare GPT, Claude, or open-weight models on the same scenarios.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCost control\u003c\/strong\u003e — benchmark token use and latency per workflow.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompliance evidence\u003c\/strong\u003e — documented test results for internal audit and customer trust.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eWhat We Build (Design + Implementation Guidance)\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eScenario libraries covering happy paths, edge cases, and known failure modes\u003c\/li\u003e\n\u003cli\u003eAutomated runners integrated with GitHub Actions, GitLab CI, or your pipeline\u003c\/li\u003e\n\u003cli\u003eTracing hooks (OpenTelemetry-compatible patterns) for production vs eval parity\u003c\/li\u003e\n\u003cli\u003eHuman review queues for subjective or high-risk outputs\u003c\/li\u003e\n\u003cli\u003eDashboards: pass rate, cost per eval run, drift over time\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eOur Process\u003c\/h3\u003e\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eInventory\u003c\/strong\u003e — catalog agent capabilities, tools, and business-critical outcomes.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMetric design\u003c\/strong\u003e — define what \"good\" means (accuracy, safety, completion rate, user satisfaction proxy).\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHarness architecture\u003c\/strong\u003e — choose assertion types, dataset format, and environment isolation.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePilot suite\u003c\/strong\u003e — 20–50 scenarios that block bad releases.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eScale-up\u003c\/strong\u003e — expand coverage, schedule runs, tie to \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eFDE\u003c\/a\u003e rollout checkpoints.\u003c\/li\u003e\n\u003c\/ol\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eEval harness specification and repository structure\u003c\/li\u003e\n\u003cli\u003eInitial scenario dataset (JSON\/YAML) with expected behaviors\u003c\/li\u003e\n\u003cli\u003eCI integration guide and quality gates\u003c\/li\u003e\n\u003cli\u003eObservability map: what to log in dev vs production\u003c\/li\u003e\n\u003cli\u003eModel upgrade playbook\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003ePairs With Agent Design\u003c\/h3\u003e\u003cp\u003eBest results come when \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e defines tool contracts and success metrics upfront — we embed eval hooks into the architecture. Already have a live agent? We reverse-engineer scenarios from logs and incident history.\u003c\/p\u003e\u003ch3\u003eRelated Services\u003c\/h3\u003e\u003cp\u003e\u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e · \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eForward Deployed Engineering\u003c\/a\u003e · \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e\u003c\/p\u003e\u003ch3\u003eFrequently Asked Questions\u003c\/h3\u003e\u003cp\u003e\u003cstrong\u003eDo you use LLM-as-judge for evaluations?\u003c\/strong\u003e\u003cbr\u003eSometimes — we combine LLM judges with deterministic checks on tool arguments, JSON shape, and policy rules to reduce false positives.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan you eval agents that call live APIs?\u003c\/strong\u003e\u003cbr\u003eYes. We design sandbox mocks, recorded fixtures, and staged environments so evals stay fast and safe.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWe use LangSmith \/ Braintrust \/ custom tools — can you work with that?\u003c\/strong\u003e\u003cbr\u003eYes. The harness design is tool-agnostic; we document integration patterns for your stack.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eHow is this different from QA testing?\u003c\/strong\u003e\u003cbr\u003eTraditional QA assumes deterministic outputs. Agent evals measure distributions, tool correctness, and safety over many runs — closer to ML ops than manual test scripts.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat if our agent is embedded in hardware?\u003c\/strong\u003e\u003cbr\u003eWe design edge-aware evals including latency budgets and offline fallbacks — see \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e.\u003c\/p\u003e\u003cscript type=\"application\/ld+json\"\u003e{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Do you use LLM-as-judge for evaluations?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"We combine LLM judges with deterministic checks on tool arguments, JSON shape, and policy rules to reduce false positives.\"}},{\"@type\":\"Question\",\"name\":\"Can you eval agents that call live APIs?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. We design sandbox mocks, recorded fixtures, and staged environments so evals stay fast and safe.\"}},{\"@type\":\"Question\",\"name\":\"Can you work with LangSmith or Braintrust?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. The harness design is tool-agnostic; we document integration patterns for your stack.\"}},{\"@type\":\"Question\",\"name\":\"How is agent eval different from QA testing?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Agent evals measure distributions, tool correctness, and safety over many runs — closer to ML ops than manual test scripts.\"}},{\"@type\":\"Question\",\"name\":\"Can you eval agents embedded in hardware?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. We design edge-aware evals including latency budgets and offline fallbacks.\"}}]}\u003c\/script\u003e","brand":"Tech Wall Electronics","offers":[{"title":"Default Title","offer_id":46479071805576,"sku":null,"price":0.0,"currency_code":"HKD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0641\/6692\/0328\/files\/Artificial_Intelligence_AI_Development_Service_-_AI_Agents_a666078b-fde8-4412-9fb9-65d0d80b30c1.png?v=1788428295"},{"product_id":"forward-deployed-engineering","title":"Forward Deployed Engineering","description":"\u003ch2\u003eForward Deployed AI Engineering\u003c\/h2\u003e\u003cp\u003eThe hardest part of AI is not the model — it is adoption. \u003cstrong\u003eForward Deployed Engineering (FDE)\u003c\/strong\u003e puts Techwall engineers beside your team during the weeks that matter: pilot launch, factory integration, executive demos, and the first production incidents.\u003c\/p\u003e\u003cp\u003eWe combine software engineering, agent operations, and — when needed — hardware\/manufacturing context from our \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e practice so deployments work in the environment you actually operate in.\u003c\/p\u003e\u003ch3\u003eWhen FDE Is the Right Fit\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eYou have a designed agent (\u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e) but lack bandwidth to integrate with legacy systems.\u003c\/li\u003e\n\u003cli\u003ePilot success requires presence on the factory floor, in clinics, or with field technicians.\u003c\/li\u003e\n\u003cli\u003eInternal teams are strong on product but new to agent observability and eval gates (\u003ca href=\"\/products\/ai-agent-harness-design\"\u003eHarness \u0026amp; Eval\u003c\/a\u003e).\u003c\/li\u003e\n\u003cli\u003eStakeholders need confidence before a multi-site or multi-region rollout.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eEngagement Models\u003c\/h3\u003e\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eModel\u003c\/th\u003e\n\u003cth\u003eBest for\u003c\/th\u003e\n\u003cth\u003eTypical duration\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePilot embed\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSingle site or department go-live\u003c\/td\u003e\n\u003ctd\u003e4–8 weeks\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRegional pod\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMulti-team rollout with shared playbook\u003c\/td\u003e\n\u003ctd\u003e3–6 months\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOffice hours + sprints\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMature internal team needing expert backup\u003c\/td\u003e\n\u003ctd\u003eOngoing retainer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\u003ch3\u003eWhat FDE Engineers Do On the Ground\u003c\/h3\u003e\u003col\u003e\n\u003cli\u003eShadow workflows and validate agent assumptions with real users.\u003c\/li\u003e\n\u003cli\u003eWire integrations, fix tool failures, tune prompts with live feedback.\u003c\/li\u003e\n\u003cli\u003eRun eval suites before each promotion to production.\u003c\/li\u003e\n\u003cli\u003eTrain champions who own the system after we step back.\u003c\/li\u003e\n\u003cli\u003eDocument edge cases discovered only in the field.\u003c\/li\u003e\n\u003c\/ol\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eRollout plan with milestones and rollback procedures\u003c\/li\u003e\n\u003cli\u003eUpdated integration code and configuration in your repos (IP stays yours)\u003c\/li\u003e\n\u003cli\u003eTraining sessions and recorded walkthroughs\u003c\/li\u003e\n\u003cli\u003eHypercare report: incidents, fixes, recommended next phase\u003c\/li\u003e\n\u003cli\u003eHandoff checklist for internal ops or managed support\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eGlobal Coverage\u003c\/h3\u003e\u003cp\u003eHeadquartered in Hong Kong with engineering and manufacturing in southern China, Techwall schedules overlap with US, European, and Asia-Pacific teams. On-site visits are arranged for pilot kickoff, critical integrations, and executive reviews; day-to-day work is often remote pair programming with your engineers.\u003c\/p\u003e\u003ch3\u003eRelated Services\u003c\/h3\u003e\u003cp\u003e\u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-harness-design\"\u003eAgent Harness \u0026amp; Eval\u003c\/a\u003e · \u003ca href=\"\/collections\/manufacturing-services\"\u003eManufacturing\u003c\/a\u003e\u003c\/p\u003e\u003ch3\u003eFrequently Asked Questions\u003c\/h3\u003e\u003cp\u003e\u003cstrong\u003eIs FDE the same as staff augmentation?\u003c\/strong\u003e\u003cbr\u003eSimilar delivery model, different outcome. FDE is outcome-focused on agent rollout and handoff — not open-ended body-shop staffing.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan FDE engineers work on our VPN and repos?\u003c\/strong\u003e\u003cbr\u003eYes, under your security policies. We follow least-privilege access and document everything we touch.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDo you offer FDE without prior design work?\u003c\/strong\u003e\u003cbr\u003eYes, but we recommend at least a short discovery so scope and success metrics are clear.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan FDE support hardware + software together?\u003c\/strong\u003e\u003cbr\u003eYes — a core Techwall differentiator. We deploy agents that interact with devices, edge gateways, and MES systems.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat happens after the embed ends?\u003c\/strong\u003e\u003cbr\u003eWe transition to your team with runbooks, optional retainer for model upgrades, or ongoing \u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e support.\u003c\/p\u003e\u003cscript type=\"application\/ld+json\"\u003e{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Is forward deployed engineering the same as staff augmentation?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Similar delivery model, different outcome. FDE is outcome-focused on agent rollout and handoff — not open-ended staffing.\"}},{\"@type\":\"Question\",\"name\":\"Can FDE engineers work on our VPN and repos?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes, under your security policies. We follow least-privilege access and document everything we touch.\"}},{\"@type\":\"Question\",\"name\":\"Do you offer FDE without prior agent design work?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes, but we recommend at least a short discovery so scope and success metrics are clear.\"}},{\"@type\":\"Question\",\"name\":\"Can FDE support hardware and software together?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. 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