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We build AI agents that actually finish work, agents that read a real request, pull the right context from your CRM, helpdesk, or documents, call the right tools, write back to the right system, and only ask a human when the decision genuinely needs one. Zygobit is an agentic AI development company building production agents and multi-agent systems for support, sales, finance, operations, and SaaS product teams, scoped in two weeks, shipped in four to eight, integrated with the stack you already run.
Agentic AI is software that pursues a goal. Instead of answering one prompt at a time, an agent reads what you want done, plans a sequence of steps, calls real tools (APIs, your database, your SaaS apps), keeps memory across those steps, and decides when to escalate to a person. The “agentic” part is the loop: observe, plan, act, check, repeat, until the goal is met or a human is asked.
In production this looks less like a chatbot and more like a junior teammate that already knows your tools. It can take a support ticket from “received” to “resolved or routed” without a person, but it hands off the moment the action is sensitive, ambiguous, or outside the policy you set.
The agent decodes intent,'refund this customer,' 'reconcile this invoice,' 'qualify this lead',and breaks it into ordered steps before touching anything.
Every action goes through a typed tool call (your CRM, helpdesk, ERP, internal API). The agent can't take an action that isn't on its tool sheet.
Short-term scratchpad for the current task, long-term retrieval over your policies and history. Agents stay coherent across 10+ steps without re-prompting.
Sensitive writes, low-confidence actions, and anything outside the policy you wrote route to a human with the full reasoning trail attached.
Seven workflow patterns where agents earn their keep. Every one of these is something we’ve either shipped or scoped in the last 12 months.
/ 01Customer Support
Triage, draft, resolve, route. Tier-1 tickets closed end-to-end; tier-2+ pre-summarized for a human.
/ 02Sales & Lead Ops
Enrich, score, follow up. Agents qualify inbound, update the CRM, and draft the next-step email with the right context attached.
/ 03Operations
Approvals, exception handling, status chase. Replace the Slack-and-spreadsheet coordination layer with an agent that already knows the SLA.
/ 04Finance
Invoice intake, three-way match, exception summaries. Agents do the boring matching; humans approve the edge cases.
/ 05HR & People
Policy Q&A, onboarding checklists, leave and benefit lookups. Backed by your handbook and HRIS, not the internet.
/ 06Product & SaaS
In-app copilots that take action, not just answer. Users get one-click 'do this for me' inside your existing UI.
/ 07Knowledge & Docs
RAG done properly: chunked correctly, retrieved with reranking, grounded with citations the user can click back to.
Traditional automation runs on rules someone wrote in advance. Workflows fire when a trigger matches. The moment reality doesn’t match the rule, a new ticket type, a slightly different invoice layout, a customer who phrases their request a new way, the workflow breaks or escalates everything to a human.
Agentic AI replaces the brittle rule with a loop that reads context and decides. A typed tool layer keeps it from doing anything you didn’t permit. Evals and approval gates keep it from doing anything you didn’t intend. The result is automation that handles the messy middle, the requests that used to land in a manager’s inbox because no rule covered them, instead of only the perfectly-shaped ones.
We don’t replace your automation stack. We pair with it. Zapier, n8n, and your existing workflows do what they’re good at (clean triggers, idempotent writes). Agents take the workflows that need judgment.
A chatbot replies. An agent acts, it reads, plans, calls tools, checks itself, and writes back to your systems.
Traditional Automation
Agentic AI
From a single agent that takes one workflow end-to-end, to multi-agent systems that orchestrate across departments. We build, integrate, and operate, not just prototype.
We design one focused agent around one observable outcome: a ticket resolved, an invoice matched, a lead qualified. Built on LangGraph or a hand-rolled state machine, typed tool layer, eval suite, monitoring from day one.
When one agent isn't enough, a planner agent, a research agent, a writer agent, a reviewer. Orchestrated with explicit handoffs, shared memory, and a top-level supervisor that knows when to stop.
Retrieval that actually retrieves. Chunking tuned to your docs, hybrid search (BM25 + embeddings), rerankers, citation grounding, and refresh pipelines that keep the index honest when your source data changes.
We pair agents with your existing automation stack, Zapier, n8n, Make, custom cron, so the deterministic parts stay deterministic and only the judgment calls go through an LLM.
Agents that talk to Salesforce, HubSpot, Zendesk, Intercom, Freshdesk, NetSuite, QuickBooks, Notion, Slack, Linear, Jira, Postgres, your internal REST API. Typed tools with input/output schemas, retries, idempotency keys, and audit-friendly logs.
Support copilots, sales SDR assistants, in-app guides. Same backend pattern, typed tools, eval gates, fallback paths, wrapped in a conversational surface (web widget, Slack, Teams, WhatsApp, or your own UI).
Role-based permissions, write-action approval queues, LangSmith or Helicone tracing, eval dashboards, hallucination and tool-error alarms. The boring layer that keeps agents safe to leave running.
We work across industries and product types. If the work is multi-step, has clear success criteria, and touches tools we can integrate with, it’s a candidate.
The honest version of the ROI story, what agents move, and what they don’t.
Most ops work isn't decisions, it's chasing status, copying between systems, and re-asking the same question. Agents take the coordination, your team takes the decisions.
Agents respond in seconds, 24/7, and never lose context mid-thread. The win is faster customers and faster cycles, not a smaller team, most clients reallocate freed-up time to the work that actually moved the metric.
An agent with typed tools across your CRM, helpdesk, ERP, and data warehouse becomes the connective tissue your SaaS stack was supposed to be.
RAG over real customer history, account state, and product usage, not 'Hi {first_name}'. Responses change because the context changed.
When ticket volume doubles, an agent-handled workflow absorbs it without a hiring round. The math compounds for any workflow that scales with customers, not employees.
Approval gates on writes, full reasoning trails, kill switches by tool. You decide which actions an agent can take on its own, and which require a human.
Eight patterns we have either shipped, scoped, or studied closely. Each one is a real production shape, not a demo.
01Agent reads the ticket, looks up the customer in your CRM, classifies the issue, drafts a reply grounded in your help center, and either sends or queues for human approval based on confidence and policy.
02Inbound lead lands → agent enriches from Clearbit/Apollo, scores against your ICP, writes the first follow-up, updates the CRM, and books a meeting on the right calendar.
03Long-context summarization, structured extraction, policy compare-and-contrast, with citations back to the source pages. Built on RAG with reranking, not 'paste it into an LLM'.
04Approvals, reminders, follow-ups, status reports. Slack-native where your team works; with structured logs so finance and audit can trace every action.
05OCR + structured extraction, three-way match against PO and GRN, exception summaries to the AP team, posting to your ERP only after a human approves the edges.
06Policy Q&A grounded in your handbook (not the internet), onboarding task automation, leave/benefit lookups against your HRIS. Role-aware so it answers managers and ICs differently.
07Embedded inside your app: 'do this for me' buttons backed by an agent that takes the action through your own internal API. Users stay in your UI; you stay in control of the tool surface.
08Long-running agents that compile competitor reports, market summaries, or internal data digests on a schedule, with cited sources and structured outputs.
Six steps. Two weeks to a working prototype. Four to eight weeks to a production agent the team can run.
30-minute scoping call: what's the goal, what tools does the agent need, what does 'good enough' look like, what's out of scope. We leave the call with one observable outcome and an out-of-scope list.
Single agent vs multi-agent. Which tools, what tool schemas, what memory pattern (scratchpad / vector / hybrid). Where the human approval gates go. What the eval suite looks like.
Source the documents, APIs, and data the agent needs. Chunk, embed, and index, with retrieval evals on day one so we know the agent is reading the right things before we add reasoning on top.
LangGraph state machine, typed Pydantic tool layer, integration adapters, prompt chains, memory wiring. Two demos per week. Evals added in parallel with each new capability.
Synthetic and real-traffic evals. Tool-failure tests, prompt injection tests, policy-violation tests. Approval-gate flow reviewed with the team that will operate the agent.
Shipped behind a feature flag, traced end-to-end (LangSmith / Helicone / Arize), monitored for tool errors and confidence drift. We stay involved through the first two weeks of live traffic.
We’re not an AI-only shop. Zygobit ships web apps, mobile apps, cloud backends, and SaaS platforms, which means the agentic AI we build is wired into a real product and a real database, not into a prototype that needs to be re-engineered before it can go live. The same team that scopes your agent can integrate it with the Postgres schema we just wrote and the Next.js front-end we just shipped.
We work primarily with founders, SME operators, and growth-stage product teams across the United States, India, and Australia, where Zygobit has offices.
We don't ship agents that work in a notebook. We ship agents with typed tool layers, retries, fallbacks, logging, tracing, and human approval flows, the unglamorous parts that decide whether an agent survives its first week of real traffic.
We'll tell you when a workflow is wrong for an agent. If the work has fewer than three steps, or no clear success signal, or volume too low to justify the eval overhead, a cron + a webhook is cheaper and we'll say so.
Web, mobile, cloud, backend, and design in one team. Your agent lands inside a product, not next to it. No second vendor to wire it into your front-end three months later.
Write actions require approval until we and you trust the agent. Read actions log to a trace. Sensitive tools route to a human queue. The defaults are conservative; you decide where to loosen them.
We don't ship agents that work in a notebook. We ship agents with typed tool layers, retries, fallbacks, logging, tracing, and human approval flows, the unglamorous parts that decide whether an agent survives its first week of real traffic.
We'll tell you when a workflow is wrong for an agent. If the work has fewer than three steps, or no clear success signal, or volume too low to justify the eval overhead, a cron + a webhook is cheaper and we'll say so.
Web, mobile, cloud, backend, and design in one team. Your agent lands inside a product, not next to it. No second vendor to wire it into your front-end three months later.
Write actions require approval until we and you trust the agent. Read actions log to a trace. Sensitive tools route to a human queue. The defaults are conservative; you decide where to loosen them.
Bring the workflow. We’ll come back with a build plan, a stack, a timeline, and a written estimate within 48 hours of the call.
The defaults we reach for, written down so you can sanity-check the team before the kickoff call.
An agent without a product is a demo. Pair this service with the rest of how we build, front-end, back-end, mobile, cloud, design, so the agent ships inside something real. Start with our AI development services, see the AI products we have shipped, or learn how the Zygobit team works.
Broader AI, ML, and generative AI development.
LLM chatbots, document AI, RAG search, copilots, and content generation.
Modern web apps with built-in AI capabilities.
iOS and Android apps with AI features.
Launch-ready SaaS platforms and copilots.
AI-powered commerce experiences.
Marketing and ops automation paired with AI.
Product and interface design for AI-driven apps.
Scale delivery with outsourced engineering teams.
Ten questions we get on almost every agentic AI scoping call.
Agentic AI services design, build, and operate AI agents that pursue goals across multiple steps, reading context, calling typed tools, holding memory, and escalating to a human at approval gates. At Zygobit this covers single agents, multi-agent systems, RAG-powered enterprise agents, and the integration, eval, and observability layers that let agents survive real production traffic.
An AI agent is software that receives a goal, breaks it into steps, calls real tools (APIs, your CRM, your helpdesk, databases), and writes back to your systems, escalating to a person when the action is sensitive or the confidence is low. A chatbot answers a prompt. An agent finishes a task.
A chatbot replies. An agent acts. The same LLM underneath, but an agent has a typed tool layer, a planning loop, memory across steps, evals between steps, and approval gates on write actions. A support chatbot tells you the refund policy; a support agent issues the refund through your billing API and updates the ticket, only if policy and confidence allow.
Generative AI produces outputs, text, code, images, summaries. Agentic AI uses generative models as one ingredient, and adds tools, memory, evals, and a control loop on top so the system can take ordered actions toward a goal. Generative AI writes the draft. Agentic AI sends the email, updates the CRM, and books the follow-up.
Customer support agents, sales SDR and lead-qualification agents, finance and AP agents, HR/people agents, ops and approval agents, in-product SaaS copilots, research and report agents, and document/knowledge agents. Each is built with a typed tool layer over your real systems and a human approval flow on the actions that matter.
Yes. We build typed tool layers over Salesforce, HubSpot, Pipedrive, Zendesk, Intercom, Freshdesk, NetSuite, QuickBooks, Notion, Slack, Linear, Jira, Postgres, and most REST APIs. Tools have input/output schemas, retries, idempotency keys, and structured audit logs, so an agent's actions are reviewable the same way a human's would be.
It can be, when it's built with the right defaults. Zygobit ships agents with role-based permissions, human approval on write actions, schema-enforced tool outputs, prompt-injection tests, eval gates between steps, full reasoning traces, and per-tool kill switches. You decide where to loosen those defaults; we wire them in conservatively by default.
Two weeks to a working prototype. Four to eight weeks to a production agent your team can run, depending on tool surface, data complexity, and approval flows. Multi-agent systems and heavily-regulated environments (finance, healthcare) take longer, typically 10 to 16 weeks, because the eval and governance surface is larger.
A focused single-agent build runs roughly $30k–$80k. A production agent with multiple integrations and approval flows lands in $80k–$250k. Multi-agent systems and enterprise governance programs start at $150k and scale with surface area. Zygobit gives a written estimate within 48 hours of the scoping call.
We're a product engineering team, web, mobile, cloud, design, and AI in one shop, so your agent gets wired into a real product, not handed off to a second vendor. We default to production patterns (typed tools, evals, approval gates, tracing) and we'll tell you honestly when an agent is the wrong tool for a workflow.
Bring one workflow. We’ll come back with a build plan, a stack, a timeline, and a written estimate within 48 hours of the scoping call.
