AI agent development
AI Agent Development Services for Your Systems
AI agent development services for teams that need a dependable production system. We scope one workflow in two weeks, define what the agent can do independently and where human approval is required, and give you a fixed quote and named engineer before development starts. We expand autonomy only after the agent proves it can handle your real cases reliably.
- trigger: demo request from the websitelead created in Salesforcerule
- tool: read_account(domain)account and history readagent
- reason: score the lead against your ICPfit written to the leadagent
- tool: draft_reply(meeting times)inside the briefagent
- gate: pricing question, rep approvesrouted to the rep in Slackperson
- done: meeting booked and loggedevery action in the logagent
- trigger: access request raisedServiceNow ticketrule
- tool: read_request(ticket)requester, system, roleagent
- reason: check against the access policyyour policy, by versionagent
- gate: the manager approves the grantqueued with the evidenceperson
- done: access granted, ticket closedaudit trail per caseagent
- trigger: submission receiveddocuments attachedrule
- tool: assemble_file(submission)documents and data pulledagent
- tool: run_rule_checks(file)each result keptrule
- reason: draft the decision and rationalenothing sent yetagent
- gate: underwriter approves or editsthe decision stays theirsperson
- Average eval score
- 97%
- To a fixed quote
- 2 weeks
- AI engineers
- 30+
- Agent functions live
- 13
What the agents run on
The models agents reason with, and the systems they act in
Reasoning model
OpenAIReasoning model
GeminiReasoning model
Orchestration
LlamaIndexDocument lookup
AWS BedrockPrivate hosting- Reasoning model
- OpenAIReasoning model
- GeminiReasoning model
- Orchestration
- LlamaIndexDocument lookup
- AWS BedrockPrivate hosting
SalesforceSales actions
ZendeskSupport actions
ServiceNowIT requests
NetSuiteFinance actions
TwilioVoice calls
SlackHuman approval- SalesforceSales actions
- ZendeskSupport actions
- ServiceNowIT requests
- NetSuiteFinance actions
- TwilioVoice calls
- SlackHuman approval
What we do
What our AI agent Development Services cover
Our AI agent development services build agents that complete tasks inside your systems, update records, and stop when human approval is required. We define the tools, limits, integrations, write access, and escalation rules before development starts. Each project begins with one function, one owner, and an evaluation set built from your past cases before the agent goes live.
- Scope
- One function, one owner
- Every engagement has one business owner on your side who approves write access and can stop the agent when needed.
- Limits
- A defined tool list
- The agent can only use the tools approved for its role, and we review that list with you before development starts.
- Proof
- Eval gate, every release
- We test the agent against your own records and release only when it clears the agreed performance threshold.
- Ownership
- Yours at handover
- You receive the code, prompts, evaluation sets, dashboards, runbooks, and training needed to run the system.
Agent library
Custom AI agents by function, industry, workflow and channel
Four ways to explore our AI agent development services: the function that owns the work, the industry it supports, the workflow where your team needs more time, or the channel it arrives through. Each card opens the page for that agent, with its own cost band, escalation rules and integrations.
- AI sales agentAnswers every inbound lead, scores it against your ICP, drafts the reply and books the meeting, with reps approving anything outside the brief.Salesforce · HubSpot · Google Workspace
- AI customer service agentHandles routine billing, account and order tickets end to end, and connects customers with a person when judgement or care is needed, with the full context attached.Zendesk · Salesforce · ServiceNow
- AI voice agentsAnswers the phone, qualifies, books and reschedules, and connects the caller with a person whenever the caller or policy calls for it.Twilio · Salesforce · Zendesk
- AI agents for marketingTurns one brief into segmented, on-brand variants, schedules them and reports what moved, with a marketer signing off before anything goes out.HubSpot · Salesforce · Snowflake
- AI agents for HRAnswers policy questions from your own handbook, runs onboarding checklists, and drafts leave and letter requests for HR to approve.Workday · ServiceNow · Slack
- AI recruiting agentSources candidates, schedules interviews, answers candidate questions and chases missing documents, and never ranks, scores or rejects anyone.Workday · Microsoft 365 · Slack
- AI agents for financeCaptures the invoice, matches it three ways, posts it and writes the audit line, so accounts payable reviews exceptions rather than every document.NetSuite · SAP · Stripe
- AI insurance agentServes policyholders and brokers on quotes, endorsements and first notice of loss, with the carrier's rules deciding what it may complete alone.Salesforce · Twilio · Zendesk
- AI underwritingAssembles the file, runs the rule checks and drafts the decision with its rationale, so underwriters approve rather than assemble.Salesforce · Snowflake · Databricks
- AI due diligenceReads the data room so analysts start at the summary: document review, company briefs and red-flag lists with the page reference attached.Microsoft 365 · Google Workspace · Snowflake
- AI agents for legalIntake, conflict checks and drafting from your own matters, inside your tenancy, with an attorney approving anything that leaves the firm.Microsoft 365 · Jira · ServiceNow
- AI agents for complianceContinuous screening and policy monitoring, with the evidence for each alert collected as it happens rather than reconstructed at audit.ServiceNow · Snowflake · Jira
- AI service deskHandles resets, access requests and how-to tickets inside your service desk, and prepares the remaining cases with context for a specialist to review.ServiceNow · Jira · Microsoft Teams
- AI agents for procurementTakes a purchase request to a purchase order without the email chain, with vendor and policy checks built into the path.SAP · NetSuite · Workday
- AI insurance agentPolicy servicing, first notice of loss and underwriting file assembly, with every drafted decision carrying its rationale.Carriers · MGAs · brokers
- AI agents in bankingKYC file assembly, dispute intake, alert triage and servicing, built to pass model risk review rather than stall in it.Retail · commercial · private
- AI agents in healthcareScheduling, intake, eligibility checks, prior authorisation packets and claims follow-up, with PHI under a BAA and every clinical call left to a clinician.Practices · health systems
- Ecommerce AI agentsOrder status, returns, refunds inside your limits, product questions and catalogue enrichment, with a person above every limit.Online · marketplaces · 3PLs
- AI agents in manufacturingQuote preparation, supplier follow-up, quality paperwork and maintenance triage, reading plant data and never writing to the control layer.Plant · back office
- AI agents for supply chainException handling across planning, procurement and replenishment, on top of the master data that decides whether any of it ships.Planning · procurement
- AI agents for logisticsCheck calls, load documents, carrier onboarding and customer updates, with rates, tenders and payments left to your team.Carriers · brokers · 3PLs
- AI agents for private equityDeal screening, portfolio KPI packs and LP questionnaires, inside your own tenancy and within what each NDA allows.Deal teams · operating partners
- AI in real estateEnquiry response, viewing scheduling, application completeness and maintenance triage inside fair housing rules, with screening, pricing and renewals left to your team.Brokerages · property managers · owners
- AI underwritingFile assembly, rule checks and a drafted decision, with the underwriter approving instead of collating.Underwriter approves
- AI due diligenceData-room review with a red-flag memo, every finding traced to the page it came from.Analyst starts at the summary
- AI contract reviewClause extraction and redlines against your own playbook, each flag tied to the clause and the playbook row that triggered it.Playbook driven
- Invoice processing automationCapture, three-way match and posting, with a person seeing only the documents that failed a check.Exceptions only
- Intelligent document processingExtraction, classification and validation over unstructured input, with a confidence threshold that routes the rest to a person for review.Confidence routed
- Conversational analyticsPlain-English questions answered from your warehouse and your agreed metrics, with the SQL shown and a refusal when a metric is undefined.SQL shown
- AI sales forecastingDeal scoring, a roll-up with a range and the forecast call brief, backtested on your past quarters before anyone relies on it.Owner commits
- Automated reportingRecurring reports built from source, every figure tied out, and the commentary approved before anything is sent.Tied out first
- AI voice agentsInbound and outbound calls with barge-in, a transcript per call and a hand-off that carries the context.Telephony · calendars · CRM
- AI chatbot developmentWeb and in-app assistants that answer from your own material and hand off with the conversation attached.Web · in-app · messaging
- AI knowledge assistantCited answers over policies, specifications and contracts, with each reader's permissions respected at query time.Microsoft 365 · Google Workspace
- A workflow that is not listedSend the workflow and the systems it touches. An engineer tells you whether an agent is the right tool for it, and what it would cost.Scoped in one call
Autonomy
How much an AI agent may do on its own
In our AI agent development services, autonomy is a dial, not a decision taken at kickoff. Production agents land at level 3 and move up only when the evaluation numbers hold on your own cases. The threshold each level has to clear starts at 95% of cases handled correctly on your evaluation set and on a weekly sampled audit, held for four consecutive weeks, agreed in scope and written into the release gate.
Suggest
The agent reads and recommends; a person takes every action. Useful in the first weeks, while the team is deciding whether it agrees with the agent, and for work where the action is trivial but the judgement is not.
Draft
The agent prepares the work (the reply, the memo, the record update) and a person edits and commits it. Most teams take the majority of their time back here, with no new risk surface, because nothing reaches a customer or a ledger unread.
Act with approval
The agent acts inside a defined envelope and stops at a gate for anything above a threshold: a value, a confidence score, a customer tier, a regulated action. This is where production agents start.
Autonomous
End to end, with review on a sample afterwards. We recommend it only for reversible, high-volume, well-instrumented work, and only once the level 3 numbers have held. Some workflows never qualify, and we say so during scope rather than after.
What that requires from us
- People involved
- Every case
- Audit trail
- Action log
- Evidence before go-live
- Scored on your past cases
- Never
- Writes to a system of record
- People involved
- Every case, at review speed
- Audit trail
- Draft plus the editor's diff
- Evidence before go-live
- Quality scored against your own resolutions
- Never
- Sends or posts without an editor
- People involved
- Exceptions and anything over a threshold
- Audit trail
- Full trace plus the approver's identity
- Evidence before go-live
- Regression suite green, shadow run beside the team
- Never
- Acts outside its typed tool list
- People involved
- Sampled quality assurance
- Audit trail
- Full trace plus anomaly alerting
- Evidence before go-live
- Stable level 3 metrics over an agreed period
- Never
- An irreversible action without a gate
Every level includes an emergency stop your team can use without us, a log of every action and a rollback path. Those safeguards remain in place at level 4.
Tech stack
The stack our AI agent development services build on, layer by layer
Opinionated, and swappable. Our AI agent development services do not resell a platform, so if your security team vetoes a vendor the architecture survives the decision.
| Layer | What we build with | How we choose |
|---|---|---|
| Models | Claude, OpenAI, Gemini, Mistral, Llama, served directly or through AWS Bedrock and Vertex AI | Per task, on accuracy against your evaluation set, then latency, then cost |
| Orchestration | LangGraph, the vendors' own agent SDKs, and plain state machines where the path is known | The least clever thing that makes a run reproducible |
| Retrieval | LlamaIndex, Postgres with pgvector, and the search your documents already sit in | Whatever your permissions model can be enforced inside |
| Integration | Native APIs and MCP servers, plus the integration platform you already licence | The path with the tightest write scopes, not the quickest to demo |
| Serving | Hugging Face, vLLM and Ollama for open-weight models you host yourself | Used where data residency or unit cost rules out an API |
| Evaluation and observability | Regression suites over your own cases, traces exported to the monitoring stack you run | Your tooling if you have it; ours only if you do not |
| Hosting | Your AWS, Azure or Google Cloud account, a private deployment, or your own hardware | Your account by default, so the system outlives the engagement |
Process
How our AI agent development services work, step by step
One workflow, weekly demos, and a stop point before the big number. You see the agent working on your data before you commit to building it.
- 012 weeks
Scope
We sit with the people doing the work, count the steps and find where the judgement actually happens. Then the tools, the write scopes, the escalation rules and the evaluation set are agreed in writing with your risk owner. You get a fixed quote and the name of the engineer who will build it.
Workflow map, autonomy spec, fixed quote
- 024–6 weeks
Prove
A working agent on your real data in a sandbox, demoed weekly. The evaluation set is built first, so good is defined before anyone argues about it, and the pilot has to clear it. If it does not, you stop with a small bill.
Working pilot, evaluation set, go or no-go
- 038–14 weeks
Build
The pilot becomes a production agent: connectors with write access, guardrails, the approval queue, admin tooling, monitoring and release gates. The evaluation suite runs on every change, and a change that regresses does not deploy.
Production agent, release gates, runbooks
- 04Ongoing
Run
Phased rollout by segment, with a person approving write actions until the numbers say otherwise. Then a clean handover to your team, or a managed service under an SLA. You own the agent either way.
Dashboards, training, handover or managed service
Cost
What AI agent development services cost
Published starting points for AI agent development services, because you cannot plan around a number you have to ask for. Build cost and monthly run cost are quoted apart, and the quote is fixed at the end of the two-week scope.
4–6 weeks
Pilot: one agent, one workflow
$8k–$35k
Test one workflow with your data, two or three read-only systems, and an evaluation set built from real cases before deciding whether to continue.
8–14 weeks
Production agent
$16k–$82k
Launch an agent with write access, guardrails, approval steps, monitoring, and release gates, ready for production use and handover.
3–6 months
Multi-agent function
$26k–$153k
Connect several agents inside one function so they can share tools, context, and escalation rules as work passes between them.
Ongoing
Managed run
$9k–$25k / month
Keep the system monitored and maintained with evaluation reruns, tuning, drift checks, and incident support under an SLA.
What moves the number
- Systems the agent writes to, not just reads: each one is a connector, a permission model and a failure path.
- Systems with no usable API, where the integration becomes most of the build.
- No labelled history to build an evaluation set from, so the first version has to be created.
- Regulated actions needing dual approval, retention rules or in-region hosting.
- Voice, which adds telephony, a latency budget and interruption handling on top of the agent.
- One named owner on your side who can decide. Nothing else takes as much cost out.
The squad
Who is on a first-agent engagement
On our AI agent development services, the people who write the code are the people you meet. The time committed by each role is agreed in the scope document so your team knows who is available and when.
| Role | Owns | Allocation |
|---|---|---|
| Agent engineer | Orchestration, tools, prompts and the evaluation harness | Set in scope |
| Integration engineer | Connectors, authentication, write scopes and sandbox parity | Set in scope |
| Workflow designer | The step teardown, the approval experience and rollout with the affected team | Set in scope |
| Delivery lead | Scope, the risk register, the weekly demo and your single point of contact | Set in scope |
| Principal engineer | Architecture review, security posture and escalation | Set in scope |
Selected work
Agents we have shipped, with the number
- Small business lending · Lending agentTime to an application's next action: 48 hours down to 18 to 24, in trial usageOne agent for merchants, funders and sales reps, each kept to their own view, with offers held inside set limits and a frustrated conversation handed to a rep.Read the case study
- Service businesses · Voice agentFirst response to a new lead: 2 to 6 hours down to under 1 minuteA voice agent makes first contact with every new enquiry by call, WhatsApp, SMS or email, writes the summary and routes the lead to a rep, who takes over the sales conversation.Read the case study
- Finance · Contract reviewTime to find a term or clause: as much as 50 to 60% lessLarge contract and payment PDFs searched in one place, with anomalies and payment discrepancies flagged for a reviewer, who decides whether each flag is a real problem.Read the case study
Security
The security questions, answered before the call
An agent with write access to a system of record is the point where an AI project becomes a security review. Our AI agent development services arrive with the scope list, the service-account model and the revocation path already drafted, so the conversation is about specifics.
Every connector runs under credentials you issue, inside your cloud account, and can be revoked by you without contacting us.
| Question | Answer |
|---|---|
| Where does our data go? | It stays inside your boundary. Documents and records are processed in your cloud account or a private deployment you approve; nothing is copied to a Meronym environment to train or tune a model. |
| Which models, hosted where? | Claude, OpenAI, Gemini, Llama or Mistral, chosen per task. Through public APIs where your policy allows, otherwise private deployments on AWS Bedrock, Azure OpenAI or Google Vertex AI, or open-weight models served on your own hardware. |
| What can the agent write? | Only the scopes on the tool list, agreed in writing before build. Anything outside them fails closed rather than degrading, and write actions sit behind an approval gate until the evaluation gate clears. |
| Who can see what it did? | Every action is logged: actor, inputs, outputs, timestamp and the reasoning trace, retained to your policy and queryable by case. Your team reads the log; so does your auditor. |
| Can we switch it off? | Yes, unilaterally, from your side. Credentials live in your secret manager; revoking them stops every connector without anyone at Meronym in the loop. |
| What compliance do you build to, and what do you hold? | We design for GDPR and HIPAA-ready patterns where they apply: data minimisation, PII classification and redaction, audit trails, and retention rules you set. We provide the current security and compliance evidence during scope, together with the DPA, so your reviewers can assess the controls directly. |
Read before you buy AI agent development services
All guides- GuideEnterprise AI agentsThe platforms, what each charges and where each stops, set against an agent built around your own systems.
- GuideAI agents for businessExamples by function and industry, and which of them hold up in production rather than in the demo.
- GuideAI agent costWhat agents, assistants and automation cost to build and to run, by tier, and the factors that move the number.
FAQs
Questions buyers ask about AI agent development services
Straight answers on what AI agent development services cost, how long they take, how they stay safe, who owns the result and what happens after launch.
How much do AI agent development services cost?
A pilot on one workflow is typically $8k–$35k. A production agent with write access to your systems is usually $16k–$82k to build, with managed operation quoted separately at $9k–$25k per month. The quote is fixed at the end of the two-week scope.
How long do AI agent development services take, from scope to production?
Allow two weeks for a scoped plan and fixed quote, followed by about four to six weeks for a working pilot on your data and eight to fourteen weeks for a production agent. The exact schedule depends on integration access, approval requirements and the number of systems the agent may update.
How should we evaluate an AI agent development company?
Ask for five things early: an indicative price band, the name or role of the engineer who would build it, evidence from a shipped agent, the written exception process, and clear ownership terms for the code. Useful answers should be specific enough for your technical, operational and procurement teams to review together.
What do your AI agent development services include, and what do we own?
The agent, the connectors to your systems, the evaluation suite, the guardrails and approval queue, the monitoring and the runbooks. At handover the code, prompts, evaluation sets and dashboards are yours, in your repositories and your cloud account, with training for the team that will run them.
How do you stop an agent doing something unsafe or unauthorised?
Three mechanisms, all set during scope. A typed tool list, so the agent can do only what its tools allow. An escalation table naming the conditions that stop it and who picks up. And an approval gate on write actions until the evaluation gate clears on your own reviewed cases. The autonomy levels above say what each level may never do.
Can the agent work inside the systems we already run?
Yes, and that is most of the build. We write least-privilege connectors to your CRM, service desk, ERP, data platform and telephony, and publish what each one reads and writes. Forty-four platforms are documented on the integrations pages, and a connector we have not built yet is a priced item rather than a surprise.
Which models and frameworks do you use, and can we switch later?
Claude, OpenAI, Gemini, Llama or Mistral, orchestrated with LangGraph or plain state machines, chosen per task on accuracy against your evaluation set, then latency and cost. The choice is written into the scope, and because the model sits behind our own interface, switching is a configuration change and a rerun of the evaluation suite.
What happens after launch?
Our AI agent development services end in one of two ways: a handover with runbooks and training for your team, or a managed service that covers monitoring, evaluation reruns on every model or prompt change, drift response and tuning under an SLA. You own the code and the evaluation sets in both cases, so leaving the managed service does not cost you the agent.
Could we build the agent ourselves instead of buying agentic AI development services?
Often, yes, and the guide on this page says how. Teams that already run their own integrations and have someone who can own evaluation usually should. What our AI agent development services are hired for is the part after the demo: write access, approval design, evaluation over your own history and the operations of it.
What is the difference between an AI agent, a chatbot and a workflow automation?
A chatbot answers. A workflow automation follows a fixed path someone drew. An agent plans over a goal, chooses which tools to call, acts in your systems and reports what it did, which is why it needs a tool list, permissions, escalation rules and evaluation that the other two do not. Agentic AI is the industry's umbrella term for the third of those, so agentic AI development services and AI agent development services mean the same work here.
Bring one workflow. Leave with a plan, a price and an engineer's name.
Tell us the workflow you want AI agent development services for, the systems it touches and what success looks like. An engineer replies within two business days with a scoped plan, the cost tier that applies and the name of the person who would build it.
Reply within two business days · Published cost bands · Speak directly with an engineer