Custom AI Agent
Development
We design, build and run AI agents that survive contact with production — with evals, guardrails and a cost ceiling, not a demo that breaks in week three.
Fixed-scope builds from $500. Full price table below — no discovery call required to see it.
You'll leave with a recommended architecture and a price range — whether or not you hire us.
Custom AI agent development is the design and deployment of an AI system built around one of your processes: it reads your data, calls your tools, and takes or recommends actions inside limits you define. The custom part isn't the model — it's the process knowledge, the integrations, the guardrails and the evaluation set.
An agent differs from a chatbot in what it's allowed to do. A chatbot answers. An agent reasons over context, calls approved tools, executes multi-step work, and escalates what it shouldn't decide alone. That capability is why the engineering around it — permissions, evaluation, monitoring — matters more than the prompt.
Most agent projects don't fail at the demo.
They fail two months later.
The prototype works on the ten examples someone tested by hand. Then it meets real inputs: the malformed PDF, the customer who asks two questions at once, the API that times out at 3am, the tool call that fires twice and creates two refunds. Nobody wrote evals, so the quality drop surfaces when a customer finds it. Nobody instrumented the traces, so debugging starts from a screenshot. Token spend triples and no one can say which step caused it.
That gap — between an agent that demos and an agent you can put in front of customers or let touch your systems — is the entire job. It's engineering: evaluation, error handling, permission boundaries, observability, fallbacks, a cost model. Not prompt tuning.
What We Build Most Often
Eight categories of custom AI agents, each designed around a specific business process. Built from scratch for your workflows.
Support Triage Agents
Read the incoming ticket, classify it, pull the account context, draft the reply, route what needs a human.
Document Intake Agents
Invoices, claims, contracts, purchase orders: extract the fields, validate against your system of record, flag the exceptions.
Research & Enrichment Agents
Take a company or a lead, gather what's public, structure it, write it into the CRM.
Reconciliation Agents
Match records across two systems that don't agree, resolve what's mechanical, escalate what isn't.
Internal Knowledge Agents
Answer staff questions from your SOPs, policies and past tickets, with citations back to the source document.
Qualification Agents
Score and route inbound demand against your actual criteria, not a generic lead score.
Compliance & QA Review Agents
Check outputs, calls or documents against a rubric and surface the ones that need review.
Scheduling & Coordination Agents
The multi-step back-and-forth that eats a coordinator's day — handled autonomously.
Is This a Fit?
We'd rather tell you on the call than after the invoice.
This is a fit if
- You have a repeatable, high-volume process where the bottleneck is reading, deciding, or routing.
- Someone internally owns the outcome and can define what "correct" means.
- You can give us access to real data, real edge cases, and the person who currently does the work.
- You want it in production, with monitoring — not a proof of concept for a board deck.
This is not a fit if
- The process isn't documented and nobody agrees on the steps. We'll say so on the call.
- A deterministic script, a rules engine, or an off-the-shelf tool would do the job. If that's what we see, we'll tell you.
- You need a website chatbot. Smaller job, cheaper providers, and they do it well.
- The accuracy floor is higher than current models reliably hit and the failure mode is unrecoverable.
When You Should Buy Instead of Build
We'd rather lose the project than build something an off-the-shelf tool does better.
Buy off-the-shelf when
The process is standard across your industry, your volume is low enough that per-seat pricing beats a build, you have no engineering capacity to own anything, or a mature product already does 90% of it.
Build custom when
The process is genuinely yours and the difference is where the value is; the agent must reach systems no vendor integrates with; your data can't leave a specific environment; per-seat pricing stops making sense at your volume; or you need to own and change the logic.
The honest middle
A lot of good outcomes are an off-the-shelf tool plus a thin custom layer where it doesn't reach. We'll say when that's the answer, and it's a cheaper engagement for you.
How We Build Custom AI Agents
Seven steps. No skipped evals. No multi-agent until the simple version is proven.
Process mapping before architecture
We sit with whoever does the work today and map the real path, including the exceptions they handle without thinking about them. Most of the value is in the exceptions. Output: a decision list — what's deterministic (script it), what needs judgment (agent), what stays human.
Evals before the build
50–150 real cases with known-correct outcomes, turned into a scored test set. This is the biggest single predictor of whether an agent project succeeds, and it's the step most vendors skip because it doesn't demo. Without it, every later change is a guess.
The simplest architecture that works
One agent, a small set of well-described tools. Multi-agent comes in only for a concrete reason — separate permission scopes, genuinely independent context, or parallelism that changes the economics.
Tool boundaries and permissions
Narrow explicit contracts per tool. Destructive or irreversible actions — payments, deletions, outbound messages — sit behind confirmation or a human by default.
Failure design
Retries with backoff, idempotency keys so a repeated call doesn't double-charge anyone, timeouts, graceful degradation, an escalation route. We write down what happens when a model provider has an outage, because one will.
Instrumentation and cost control
Every run traced, every tool call logged, tokens and latency per step, spend capped, alerts on quality and cost drift. You get the dashboard.
Pilot on real traffic, then scale
Shadow mode or a limited slice first, measured against the eval baseline and against the people doing the task now. We scale when the numbers hold, not when the deadline arrives.
What You Get
Every engagement ships with these deliverables. No hidden extras.
A scoped agent design
What it decides, what it must never decide, which tools it can call, what it hands to a human.
The working agent
Deployed in your environment or ours, integrated with your systems.
An evaluation set
From your real cases, with a scored baseline — so "is it working?" is a number.
Guardrails
Input validation, tool permission boundaries, output checks, injection defences.
Observability
Traced runs, logged tool calls, tokens and latency per step, alerting.
Human-in-the-loop paths
For what should escalate rather than be guessed at.
Runbook and handover
Architecture, failure modes, how to change prompts safely, how to re-run evals.
Source code and prompts
Owned by you. Full IP transfer on completion.
Where Your Data Goes
Straight answers. No hand-waving.
Deployment Options
Your cloud account, VPC, on-premise, or our managed hosting. You choose the model that fits your security posture.
Training Data Policy
Your data is never used to train our models or any third-party models. Never. We enforce zero data retention policies with all AI model providers.
Retention & Deletion
Agent traces are logged for debugging and optimization. Retention periods are configurable. Full purge on request.
Access Control
Least-privilege credentials per tool, scoped API keys, audit logs of every agent action.
PII Handling
Field-level redaction and masking for sensitive data. PII never enters model context unless explicitly required and approved.
Prompt Injection Defence
Anything the agent reads from outside — web pages, emails, uploaded documents, tickets — is treated as data, never as instructions. Injection attempts are part of the test suite.
Our Stack
We stay model-agnostic where it's practical and keep the model behind an interface, so an upgrade is a re-run of your eval set rather than a rebuild.
| Category | Technologies |
|---|---|
| AI Models | Claude, GPT-4, Gemini, Llama / Mistral via Ollama |
| Orchestration | LangChain, AutoGen, LangGraph, Custom |
| Knowledge / RAG | Pinecone, Weaviate, ChromaDB, Custom NLP |
| Evaluation | LangSmith, custom eval harnesses |
| Tracing & Observability | LangSmith, custom telemetry dashboards |
| Infrastructure | Vercel, AWS, Google Cloud, Docker, K8s |
| Integrations | MCP servers, direct APIs, Salesforce, HubSpot, Slack, Notion |
What It Costs
Most agencies quote after three calls. We publish the range, because the variables that move the price aren't a secret and your time isn't free.
| Feature | Starter | Most PopularGrowth | Professional | Enterprise |
|---|---|---|---|---|
| Build Fee | $500 – $1.5k | $3k – $8k | $8k – $15k | Custom Quote |
| Agent Capacity | 1 dedicated agent | 3 – 5 agents | 5 – 10 agents | Unlimited scale |
| System Integrations | Up to 3 | Up to 10 | Unlimited | Unlimited |
| Evals & Monitoring | Basic | Full eval suite | Full eval suite | Custom telemetry |
| Security / Deployment | Secure Cloud | Cloud / On-prem | Cloud / On-prem | Air-gapped Priority |
| Included Support | 30 days SLA | 60 days SLA | 90 days SLA | Custom SLA Retainer |
What moves the price
How many systems the agent integrates with, whether your data is accessible and clean, how many exception paths need handling, compliance and hosting requirements, and whether we run it or hand it over.
What's not included
Model and infrastructure usage (billed to your own accounts, so you see the real number), third-party licences, and work on systems we didn't build.
Straight Answers
The concerns we hear most, addressed directly.
"We've already had one agent project fail."
Ask what the eval set looked like. Usually there wasn't one — so nobody could tell whether a change made it better or worse. That's a fixable process failure, not proof the use case doesn't work. We'll review what you have, and if the use case is genuinely wrong we'll say that rather than rebuild it.
"How do we know it won't do something stupid at scale?"
Irreversible actions sit behind explicit boundaries. Low-confidence cases escalate instead of guessing. Spend is capped and every run is traced. We'd rather ship an agent that hands 15% to a human than one that silently gets 15% wrong.
"Isn't this obsolete when the next model ships?"
The model is one swappable component. Process mapping, evals, tool contracts, guardrails and monitoring carry over. A model upgrade becomes a re-run of your eval set — which is exactly what the eval set is for.
"Can our team maintain it?"
That's the intent: source code, prompts, runbook and a handover session. We also offer optional support retainers for ongoing monitoring and optimization.
"Can you work with our in-house team or existing provider?"
Yes. Some engagements are a build, some are an audit and rescue of an agent that isn't working, some are an embed alongside your engineers.
"What if it doesn't work?"
Every engagement starts with a scoped pilot phase. If the agent doesn't hit agreed-upon metrics during the pilot, you decide whether to proceed. We don't lock you into a full build before the pilot proves the value.
Written by
Siddhant Singh Rao
Founder & Lead Engineer at Agents Chef. Google-certified in MLOps and Responsible AI. Has built and deployed production AI agent systems across e-commerce, healthcare, finance, and SaaS — from single-workflow support agents to multi-agent orchestration platforms.
Frequently Asked Questions
Building an AI system that takes actions on your behalf — calling tools, reading and writing to your systems, making routing or classification decisions — designed around one specific process rather than as a general assistant. The custom part is the process knowledge, integrations, guardrails and evaluation set.
A chatbot responds to what someone types, usually within a scripted or single-turn exchange. An agent reasons over context, calls approved tools, executes multi-step work across systems, and escalates what it shouldn't decide alone. The difference is what it's permitted to do, which is why the engineering around permissions and evaluation matters more than the prompt.
Buy when the process is standard, volume is low, or a mature product covers most of it. Build when the process is genuinely yours, the agent needs systems no vendor integrates with, your data can't leave a specific environment, or per-seat pricing stops making sense. Often the right answer is a tool plus a thin custom layer — we'll say so when it is.
A scoped single-agent build with two or three integrations is typically 1–2 weeks. Multi-system work with compliance review runs 4–8 weeks. The scoping call gives you a date range, not a guess.
Ours is published above. Across the market expect wide variance driven by integration count, data readiness, and whether evaluation and monitoring are included — many quotes look cheaper because they exclude exactly the work that keeps an agent running.
Usually not at the start. One agent with clear tools handles more than people expect. When multi-agent genuinely earns its place is when there are separate permission scopes, independent context, or parallelism that changes the cost — starting there adds failure modes before the simple version is proven.
Your data is never used to train our models or any third-party models. We enforce zero data retention policies with all AI model providers. For clients with strict data sovereignty requirements, we offer on-premise deployment where your data never leaves your servers.
Claude, GPT-4, Gemini, and open-source models via Ollama for LLMs. LangChain, LangGraph, AutoGen, and custom orchestration for agent coordination. Pinecone, Weaviate, and ChromaDB for retrieval. Model-agnostic where practical, with the model behind an interface so upgrades are an eval re-run.
Yes. Source code, prompts, evaluation sets, and runbooks are fully transferred to you on project completion.
External content — web pages, emails, uploaded documents, tickets — is treated as data, never as instructions. Tools have narrow permissions, irreversible actions need confirmation or a human, and injection attempts are part of the eval suite.
Yes. We do builds, rescues of agents that aren't working, and team embeds where our engineers work alongside yours.
Find Out What Your Agent Would Actually Cost
Book a 30-minute call and bring the process you want automated. You'll leave with a recommended architecture, the rough price from the table above, and an honest answer about whether an agent is the right tool — including if it isn't.