01
Agent strategy and use-case scoping
We rank your workflows by value and risk, pick the one to automate first and agree what success looks like before any code is written.
FIG 01 · AI Agent Development
AI agent development company: custom agents that do real work
Agents that plan, use your tools and finish the task, with guardrails, approval steps and evals built in. From the team that built the agent and MCP access inside Kenerate AI.
See how it works
FIG 02 · Quick answer
What does an AI agent development company do?
An AI agent development company designs, builds and runs software agents that plan steps and use your tools, data and APIs to finish tasks, not just answer questions. Elgrith builds custom AI agents with guardrails, human approval steps, evaluation tests and MCP integrations. We built the agent and MCP access inside Kenerate AI, our flagship product.
- AI models in Kenerate AI, our flagship build
- 200+
- AI apps that reach Kenerate through MCP and CLI
- 19+
- Projects delivered since 2015
- 1,000+
- Your code, prompts and IP
- 100%
FIG 03 · Why it matters
Agents are on every roadmap. Few reach production.
From Stanford HAI’s AI Index 2025 and an IBM and Morning Consult survey of 1,000 enterprise AI developers.
01
78%
Of organizations use AI
78% of organizations reported using AI in 2024, up from 55% the year before.
Source: Stanford HAI, AI Index 2025(opens in a new tab)02
99%
Of developers are exploring agents
99% of developers building enterprise AI applications said they are exploring or developing AI agents.
Source: IBM Think, AI agents in 2025(opens in a new tab)03
Pilot first
One workflow, measured
We prove one agent on one real workflow, scored against an eval set, before anything scales.
FIG 04 · Definition
What an AI agent is (and isn’t)
An AI agent is software that uses a language model to decide its next step, calls tools and APIs to act, checks the result and keeps going until the task is done. A chatbot answers; an agent acts. Here is when each option fits.
| Chatbot | AI agent | Workflow automation (n8n, Zapier) | RPA | |
|---|---|---|---|---|
| Best for | Answering questions from your content | Multi-step tasks that need judgment and tools | Fixed, repeatable steps between apps | Legacy screens with no API |
| Autonomy | Low: it replies, a person acts | Medium to high, inside the limits you set | None: it follows the rules you wrote | None: it replays recorded clicks |
| Example | “What is your refund policy?” | Research a lead, update the CRM, draft the email for approval | New form entry → CRM record → Slack alert | Copy invoice data out of an old desktop system |
| When not to use | When the user needs something done, not explained | When the steps never change: automation is cheaper | When inputs are messy and need reading and judgment | When an API exists: integrate instead |
The best systems often combine them: automation for the fixed steps, an agent for the judgment calls.
Only need answers? See AI chatbot developmentFIG 05 · What you get
AI agent development services
Six services, one goal: an agent that finishes real tasks safely, measured against the work your team does today.
02
Custom AI agent development
Single agents and multi-agent systems with tool calling, memory and planning, on the model and framework that fits each task.
03
MCP server development and tool integrations
MCP servers and API connectors for your CRM, ERP, helpdesk and internal systems, so your agents, and assistants like Claude, ChatGPT and Cursor, can use them.
04
RAG and knowledge agents
Agents that search your documents, tickets and databases, cite their sources and respect who is allowed to see what.
05
Guardrails, governance and human-in-the-loop
Least-privilege permissions, approval gates for irreversible actions, audit logs and a kill-switch, designed in from the first sprint.
06
Evaluation, observability and optimization
Eval sets built from real tasks, tracing on every run and regression tests before each release, so quality and cost are measured, not guessed.
Deliverables
12
deliverables, in writing
Included in every build, written into your fixed quote before work starts.
FIG 06 · Use cases
Agents we can build for you
Each one starts with a trigger, uses the tools you already have and ends with an output a person can check.
- Helpdesk ticket
- Web form
- Slack message
Your agent
- CRM
- ERP and accounting
- Database
- Human approval
Sales research agent
- Trigger
- A target account is added
- Tools
- Web search, CRM, your notes
- Output
- Account brief and a draft email for approval
Lead qualification agent
- Trigger
- Inbound form or chat
- Tools
- CRM, enrichment API, calendar
- Output
- Scored lead, the right owner, a booked call
Support resolution agent
- Trigger
- A new helpdesk ticket
- Tools
- Zendesk or Freshdesk, order system, knowledge base
- Output
- A resolved ticket, or a clean handoff with context
Back-office data entry agent
- Trigger
- An invoice, PO or form arrives by email
- Tools
- Document parsing, ERP, accounting
- Output
- A validated record, with exceptions flagged
Reporting agent
- Trigger
- A weekly schedule, or a question in Slack
- Tools
- Database, analytics, spreadsheets
- Output
- A written summary with charts and sources
Recruiting screening agent
- Trigger
- A new application
- Tools
- ATS, your job criteria, calendar
- Output
- A shortlist with reasons. A person makes the call
Ops monitoring agent
- Trigger
- An alert or anomaly
- Tools
- Logs, metrics, runbooks, ticketing
- Output
- A diagnosis, a suggested fix and an incident ticket
MCP server for your product
- Trigger
- A user asks Claude, ChatGPT or Cursor
- Tools
- Your MCP server and API
- Output
- Your product’s actions, inside their AI app
FIG 07 · Flagship build
Proof: Kenerate Agent and MCP
We designed and engineered Kenerate AI, an all-in-one AI image and video studio. Its agent and its MCP and CLI access are the same patterns we build for clients.
- Kenerate Agent: chat with an agent that picks the model and runs the workflow for you
- MCP & CLI: use Kenerate inside Claude, ChatGPT, Cursor and 19+ AI apps
- 200+ AI models behind one app, one login and one credit balance
- 23+ studios and tools, from Image Studio to Director
Agent: Kenerate inside Claude, ChatGPT and CursorDesigned and engineered by Elgrith
- AI models in one app
- 200+
- Studios and tools
- 23+
- Maximum output
- 4K
- Guides
- 500+
FIG 08 · Safety
How we keep agents safe
An agent that can act can also act wrongly. These controls are part of every build, not an add-on.
07principles
Least-privilege tool access
Each tool gets only the scopes it needs, with read and write access kept separate.
Prompt-injection defenses
Content from emails, web pages and files is treated as data, never as instructions, and risky outputs are filtered.
Approval gates
Refunds, payments, deletions and outbound messages wait for a person to approve them.
Audit logs
Every step, tool call and decision is logged, so you can see what the agent did and why.
Rate and spend limits
Caps on actions, tokens and cost, per run and per day.
Kill-switch
One switch pauses the agent and hands the work back to your team.
Data residency and privacy
US, UK or EU cloud regions, with data handling designed around UK GDPR and US privacy laws.
FIG 09 · Measurement
How we measure an agent
Before launch we build an eval set from real tasks your team has already done. Every release is scored against it.
| Metric | What it tells you | How we measure it |
|---|---|---|
| Task success rate | Share of tasks finished correctly, end to end | Scored against the eval set by rules and LLM-as-judge, with human spot checks |
| Escalation rate | How often the agent hands work to a person | Logged on every run and reviewed with you |
| Tool-call accuracy | Whether it picked the right tool with the right inputs | Traced step by step in Langfuse or LangSmith |
| Latency | How long one task takes | Median and slowest runs, per task type |
| Cost per task | Model and API spend for one finished task | Tokens and API calls counted per run |
| Regressions | Whether a change made anything worse | The full eval suite runs before each release |
This is the scorecard we set up for every agent. Targets are agreed with you during the pilot; we don’t quote benchmark numbers before we have seen your data.
FIG 10 · Process · 5 steps
Pilot first, then scale
One workflow, proven on real data, with a fixed written quote for each phase. Production comes only after the evals pass.
- Step 01
Discovery: one workflow
We map one workflow end to end: the trigger, the tools, the data, the risks and what “done” means. You get a written scope and a fixed quote.
- Step 02
Prototype on real data
A working agent on your real tools and data, plus the eval set that scores it. You see quality in numbers, not demos.
- Step 03
Pilot with humans in the loop
Your team uses the agent with approval gates on. We tune prompts, tools and models until the scorecard holds.
- Step 04
Production rollout
Permissions, limits, logging and monitoring go live in cloud accounts registered to your business, rolled out in stages.
- Step 05
Monitor and improve
We watch real runs, add new cases to the eval set and swap in better or cheaper models as they arrive.
FIG 11 · What you get
Everything included
Written into your fixed quote, so you know exactly what you are paying for before work starts.
12items
- Workflow map and use-case scoring
- Written scope and fixed quote per phase
- Working agent on your tools and data
- Eval set built from real tasks
- Agent scorecard and tracing
- MCP server or API connectors, where needed
- Guardrails, approval gates and kill-switch
- Audit logs, rate and spend limits
- Cost-per-task estimate and spend dashboard
- Deployment to your own cloud accounts
- Runbook and handover for your team
- Full source code, prompts and documentation
FIG 12 · Tech & tools
The tools behind the work
Proven, well-supported tools, chosen for your project and set up in accounts your business owns.
- OpenAI
- Anthropic Claude
- Google Gemini
- Llama
- Mistral
- Model Context Protocol
- LangGraph
- OpenAI Agents SDK
- CrewAI
- Vercel AI SDK
- PostgreSQL + pgvector
- Pinecone
- Langfuse
- LangSmith
- OpenTelemetry
- n8n
- AWS
- Azure
- Google Cloud
FIG 13 · Who it’s for
Agents for every kind of team.
Model-agnostic, built in your stack, and run on your hours in the USA and UK.
3 audiences
SaaS & startups
Agent features your users pay for.
Add an agent to your product, or ship an MCP server so your customers can use you from Claude, ChatGPT and Cursor.
- In-product agents behind feature flags
- MCP server and CLI for your API
- Usage limits and metering per account
- Code in your repository from day one
Operations
Take the copy-paste out of the back office.
Agents for data entry, reporting, triage and research, with a person approving anything that matters.
- Start with one measured workflow
- Works with your CRM, ERP and helpdesk
- Approval gates on every risky action
- Clear escalation to your team
Enterprise
Governed agents that pass security review.
Least-privilege access, audit trails and regional hosting, documented for your security and risk teams.
- SSO, roles and audit logs
- US, UK or EU data residency
- Azure OpenAI or AWS Bedrock deployments
- Written controls mapped to your AI policy
FIG 14 · Working with us from the USA and UK
Built in New Delhi. Run on your hours.
Every project gets a point of contact in New York or London and an engineering team in New Delhi. Here is how today’s working days line up, live.
New York
North America
--:--ET
New Delhi team Your working day- Shared working time
- ≈ 3 hours today
- New Delhi is
- 10.5 h ahead
- Live calls, your time
- 9 a.m. to 12 p.m. ET
London
UK & Europe
--:--UK
New Delhi team Your working day- Shared working time
- ≈ 4.5 hours today
- New Delhi is
- 5.5 h ahead
- Live calls, your time
- 9 a.m. to 1:30 p.m. UK
Three ways to work together
- 01
Fixed-scope project
A written scope, one fixed price and a dated plan. Suits websites, apps and MVPs.
- 02
Dedicated team
Designers and engineers on your roadmap every month, billed monthly.
- 03
White label
For US and UK agencies: our work, your brand, unbranded deliverables.
Every engagement
- Fixed quote in USD, GBP or INR after a free 30-minute call
- NDA before discovery. You own all code, designs and content
- A working demo every sprint, not status slides
- Reply within one business day, calls inside your hours
Book a call
Calls on ET or UK hours
FIG 15 · Compare
Custom agent, agent platform or in-house build?
| Custom agent (Elgrith) | Off-the-shelf agent platform | In-house team | |
|---|---|---|---|
| Best for | Workflows across your own tools and data | Common tasks inside one vendor’s apps | AI as a long-term core competency |
| Fit to your tools | Any API, database or MCP server | The platform’s connectors | Full control |
| Safety and evals | Approval gates, logs and evals built in | The vendor’s settings | Depends on the hire |
| Data control | Your cloud, your model choice | Vendor-hosted | Yours |
| Time to start | After a free call and a written scope | Same day, for supported tasks | Months of recruiting |
| Ownership | Code, prompts and IP assigned to you | Licensed | Yours |
FAQ
Frequently asked questions
Straight answers about AI Agent Development with Elgrith.
A chatbot answers questions. An AI agent plans and takes actions: it calls your tools and APIs, checks the result and keeps going across several steps until the task is done. Many projects start as a chatbot on your own content and grow into an agent once it is clear which actions it should take, and with what approvals.
FIG 16 · Related
Keep exploring
Free 30-minute call
Pick one workflow. We’ll turn it into an agent.
Tell us the task that eats your team’s week. After a free call you get a written scope and a fixed quote for the pilot.
Pick a day for your call
OpenPick a day. You'll choose a time in your own time zone on the next step.