Custom AI agents we build
Customer facing action agents
Agents that don't just answer — they issue the refund, change the order, and book the appointment, then escalate with full context when a human is genuinely needed.
Back office & operations agents
Reconciliation, data entry, and status chasing handled end to end — the repetitive work your team does between systems, done in seconds instead of hours.
Document processing agents
Ingest contracts, statements, and forms, extract what matters, and produce a decision you can defend — with every output traced back to its source document.
Lead & sales agents
Every inbound lead engaged, enriched, qualified against your criteria, and booked into your CRM — before a competitor has even replied.
Internal copilots that take action
Knowledge agents grounded in your data (RAG) that don't stop at answers — they update the ticket, draft the reply, and trigger the workflow on your behalf.
Multi step workflow orchestration
Agents that plan across several tools and APIs, call each one in the right order, check their own work, and only ask a human when the stakes call for it.
AI agent development case studies
See the AI agent we built for Future Mortgage — it ingests W-2s, paystubs, and employment records, produces a regulator grade decision in minutes instead of 48+ hours, and automates 80% of the manual background check work with every decision traceable to its source.
Read full case study
See the financial-API agent we built for a fintech client — it reasons over live financial data through MCP and reaches CFO-grade conclusions with 93% precision in about 5.5 minutes, for under $1 per query.
Read full case studyHow our AI agent development process works
Days 1–5: a demo on your workflow
We pick one high-volume workflow, connect a sample of your real data and tools, and show you an agent completing the task — before you commit to a build.
Weeks 2–8: proof of concept
The agent runs against real cases with guardrails and human approval where you want it. You get accuracy and autonomy benchmarks plus an architecture plan and budget.
Production in two-week sprints
We harden integrations, permissions and the audit trail, ship to production, then keep evaluating every corrected case so coverage grows sprint over sprint.
What an AI agent does for you
Takes action, not just answers
A chatbot replies; an agent completes the task — refunds issued, records updated, tickets closed — so work gets done, not deflected to a queue.
Works across your whole stack
It reads from and writes to the tools you already run — CRM, helpdesk, ERP, calendars, internal APIs — instead of trapping the work in one more silo.
Runs 24/7 at scale
One agent handles the same volume as a room of people, at 2am and at month end peak alike, without the queue or the overtime.
Every decision is traceable
Each action references the data and policy it acted on, so you get a regulator grade audit trail instead of a black box you have to trust blindly.
Escalates with full context
When something falls outside its guardrails, it hands off to a human with the whole thread and its reasoning attached — no starting from zero.
Gets sharper over time
Every resolved case and correction feeds evaluation, so accuracy and coverage climb sprint over sprint against the KPI you set at kickoff.
Why companies choose DestiLabs as their AI agent development company
- Manual work eliminated — the repetitive majority of a process automated, like the 80% we removed from background checks for Future Mortgage.
- Turnaround cut from hours or days to minutes, on the workflows that currently bottleneck your team.
- A regulator grade audit trail — every decision traceable to the source data and policy it acted on.
- Full ownership — code, prompts, models, and documentation are yours. No per seat subscription, no vendor lock-in.
- Numbers you can show the board — monitoring, a /status page, and the KPI line we agreed on at kickoff moving in the right direction.
Industries we've shipped in
Engagements that fit the stage you're in
Discovery call
A clear read on whether an AI agent would pay back in your workflow, which process to start with, and what it would cost.
Ideal for:
- A live business with a repetitive, high volume process to automate
- Non-technical founders or executives who need tech validation
- Teams comparing AI agent development companies before committing
Agentic system PoC
A working agentic system on your data, integrated with your tools — live demo within the first 5 days, validated against your real workflow.
Ideal for:
- You want an agent taking real action but need proof on your data first
- An off the shelf or no code tool broke on your real workflow
- You need accuracy and containment numbers to greenlight the build
Deliverables:
- Working agentic system on your data
- Live demo within 5 days
- Accuracy and autonomy benchmarks
- Integration and architecture plan
- MVP scope and budget you can take anywhere
Frequently asked questions
AI agent development is building software that uses large language models to plan and take actions toward a goal — calling tools, APIs, and databases, checking its own work, and escalating when needed. Unlike a chatbot that only responds, an agent is developed to complete multi step tasks inside your systems, grounded in your data and bounded by guardrails you control.
A chatbot answers questions; an AI agent takes action to complete the task. Agents plan across steps, call your tools and APIs, and actually book, refund, verify, or escalate — a chatbot mostly deflects. We break down when each one fits in AI Agent vs Chatbot.
End to end: picking the workflow with the clearest payback, designing the agent's tools and permissions, connecting it to your systems, building the evaluation set from your real cases, adding guardrails and human approval steps, shipping it to production, and monitoring it afterwards. You get the code, prompts and documentation — not a subscription to someone else's platform.
Use a platform when your workflow is standard and the platform already integrates with your tools. Build a custom AI agent when the workflow is specific to your business, touches several internal systems, needs an audit trail, or when per-seat or per-action pricing will cost more than owning the agent. We score both options in Custom AI Agent Development: Build vs. Buy.
Through the same APIs and integrations your team already uses. We connect agents to your CRM, helpdesk, ERP, databases, calendars, and internal services — via native integrations, REST APIs, or protocols like MCP — with scoped, least privilege access. The agent reads from and writes to your systems of record, so nothing lands in a silo.
Yes, when they're built with the right guardrails — and that's how we build them. Agents run with scoped permissions, policy checks before high stakes actions, human in the loop approval where you want it, and a full audit trail for every decision. You decide exactly which actions an agent can take on its own and which need a human sign off.
AI agent development starts with a proof of concept on your own data: $10k–35k over 4–8 weeks. A full production build is quoted per scope after that, once the agent has proven itself on your workflow. We break down exactly where the money goes in our guide, How Much Does It Cost to Build an AI Agent in 2026.
The ones that pay back fastest are document processing, back office automation, support resolution, lead qualification, and internal copilots that take action. We ranked nine of them with real examples, honest costs, and ROI in Top 9 AI Agent Use Cases for Business in 2026.
You see a working demo on your own data within the first 5 days. A validated proof of concept takes 4–8 weeks, and most production rollouts follow within a few two week sprints, depending on how many tools the agent integrates with and how autonomous you want it to be.
Let's scope your AI agent
Book a 30-minute call. We'll map your workflow, tools, and data, and tell you honestly whether an AI agent moves your numbers.