01
Claims and case resolution agents
Investigates a denial or exception across several systems, forms a root-cause hypothesis, assembles the response, routes it for approval.
AI Agent Development Company
MindInventory builds custom AI agents that complete multi-step work: systems that plan a task, call the tools and APIs your business already runs on, and take action inside your systems of record. We build them for enterprise workflows where a wrong action changes a record, a claim, or a dispatch, not just a conversation.
Agentic AI development is what we do daily, not a line we added last year. Six-agent clinical orchestration, denial resolution with a human approval gate, and governed industrial agents whose directives trace back to the telemetry behind them.
Trusted By Global Clients, Including Fortune 500 Companies
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Authorization designed first
Every tool the agent can call is classified by reversibility before the first prompt is written.
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PoC on your data
No production build starts without a proof of concept running your real workflow.
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The evaluation harness is yours
Trajectory tests, tool-call assertions and the CI suite transfer at handover.
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Monitored after launch
Completion rate, escalation rate and cost per completed task, tracked continuously.
70+
AI and ML specialists
300+
Engineering specialists
2700+
Projects delivered
1800+
Clients served
15+
Years in business
ISO 42001: 2023
ISO 27001: 2022
ISO 9001: 2015
SOC2 Type II
HIPAA
GDPR
AI Agents We Build
Agents earn their cost where the work has real branching, a measurable outcome someone already owns, and systems that can actually be reached. Below are the agent types we build most often, grouped by the work they take over.
02
Clinical documentation and coding agents
Produces the note, the code recommendation and the billing record directly in the EHR, with provider review before anything is filed.
03
Customer service and support agents
Resolves tier-one tickets end to end by acting in the CRM and order systems, escalating anything outside its envelope.
04
Back-office and finance operations agents
Invoice matching, reconciliation, exception handling and payment preparation, with the payment itself gated.
05
IT service desk and incident agents
Triages alerts, correlates logs and metrics, runs diagnostic tooling, drafts the remediation for an engineer to approve.
06
Monitoring, inspection and dispatch agents
Watches sensor, telemetry or event streams, decides what warrants a response, and initiates it.
07
Research and analysis agents
Multi-source retrieval and reasoning over your own documents and data, with citations and a traceable path.
8
Sales and revenue operations agents
Enrichment, scoring, routing and CRM hygiene, so the pipeline data is correct before anyone acts on it.
Where we push back
The open-ended assistant with broad tool access and no defined workflow. It demos well and is close to unevaluable, because there is no task definition to test against.
Enterprise AI Agent Development Services
“AI agent development” covers several distinct engagements, and knowing which one you are actually buying changes the cost, the timeline, and who needs to be in the room. They are stages of one lifecycle rather than alternatives, and most organizations enter at a different point than they expect.
AI agent consulting applies when the workflow is not yet chosen. The work is mapping where the branching and the cost sit, sizing the return, and producing an autonomy target and a build-or-wait recommendation. Buying a build before this is how teams end up automating the process that was easiest to describe rather than the one that was expensive.
Custom AI agent development is the build itself, and it is closer to AI agent software development than to model work: reasoning, planning, memory, tool contracts and error handling designed around one workflow. Custom matters when the process is specific to your business or regulated. Where a workflow is generic, a platform agent configured well beats a bespoke one, and we will say so.
Enterprise AI agents describes deployment context, not different technology. It means agents operating inside SSO and role-based access, under procurement and audit requirements, against systems of record that cannot be replaced. That context, rather than model capability, is what separates a two-month build from an eight-month one.
AI workflow agents sit at the boundary with deterministic automation, handling processes that are mostly fixed with genuine exception branches. The agent handles the exceptions; the pipeline handles the rest.
Multi-agent systems apply when one agent’s tool set, context budget or permission scope stops being coherent. Detail in the orchestration section below.
AI agent integration is usually the largest line item: tool contracts, authentication, permission scoping and error semantics for every system the agent touches.
AI agent modernization covers replacing brittle rule-based automation, or rebuilding an early assistant that answers questions but cannot act, with something evaluable and governed.
Ai Agents We Have Put Into Production
Every project below is a production system with a named client, and the numbers come from after launch, not from a pilot. Most are still running, and several have been with us for years.
Sully AI, clinical documentation and coding agents
Six agents covering reception, triage, scribing, clinical consultation, medical coding and care coordination, with an orchestration layer that shares patient context and routes handoffs. Native Epic and athenahealth integration, HIPAA-ready, role-based access, full auditability. No drafted note, suggested code or triage recommendation enters the patient record without a licensed provider reviewing it. Each agent runs in an isolated container, so the coder can be retuned without touching the scribe.
Outcomes:
12.5M+ minutes of clinical documentation automated
Providers handling twice the workload without additional hours
Arrow, claim denial resolution agents
A six-step agentic workflow: ingest, diagnose, prepare, review, act, track. The agents identify likely root cause by matching against historical resolutions rather than exact denial codes, draft the fix, and assemble a ready-to-submit appeal package. A biller approves before anything leaves the system. The queue is ranked by dollar value, aging and recovery probability.
Outcomes:
Claim denials reduced by 85%
Accounts receivable days cut from 45 to 18
1.5B+ claims processed through the platform
Korial, governed industrial inspection agents
A hardware-agnostic intelligence layer above robots, drones and fixed sensors from several vendors, with Unreal Engine 5 digital twin simulation so autonomous missions are validated against a spatially accurate model of the site before deployment to live equipment. The agentic layer converts raw multimodal telemetry into bounded, auditable directives. Every directive traces back to the telemetry that produced it. Clients include Shell, BP and Evonik.
Outcomes:
Inspection costs reduced by 28%
40,000+ human inspection hours saved
1M+ autonomous inspections completed
Your workflow is not one of these three.
Two weeks
At no cost
Tells you whether an agent fits it.
How We Build and Ship AI Agent
Five stages. The order matters more than on a model build, because integration and authorization are first-phase concerns on an agent and last-phase concerns on almost everything else.
01
Workflow and authorization mapping (1 to 2 weeks)
We map the real process, not the documented one. Every system the agent would touch is inventoried, every action is classified by reversibility, and the business metric it must move is named with its owner. Ends in a written recommendation and a fixed estimate.
02
Tool and integration design
Tool contracts specified before the agent is built: purpose, arguments, error semantics, permission scope, dry-run behavior. Where a system has no usable API, the real cost of the project surfaces here rather than in month five.
03
Evaluation harness, then the agent
The benchmark set comes from your real cases, with expected outcomes agreed by your domain experts, before the agent exists. A test written after the system is shaped by what the system happens to do.
04
Proof of concept on your data (6 to 10 weeks)
The agent runs the real workflow against real data, at draft-level authority by default. Output is a measured completion rate, escalation rate and cost per completed task. The production decision is made against those numbers.
05
Production hardening and handover
Observability on every tool call, injection filtering on retrieved content, confirmation gates on irreversible actions, cost monitoring, regression suite in CI. Then the harness, pipelines, prompts and documentation transfer to you.
Agents drift for reasons models do not. A payer changes a form, an API adds a required field, an upstream system returns a new status code. None of that shows up in model metrics, which is why monitoring continues under MLOps Consulting.
Why AI Agent Pilots Stall Before Production
Agent pilots rarely fail on model quality. A demo runs one path. Production runs ten thousand, and the gap between an 85% completion rate and a 99% one is error handling, termination logic, state coherence and a defined escalation route.
Multi-step reliability compounds
If each step succeeds independently at rate r, the whole task succeeds at r to the power of n.
Per-step success
90%
95%
98%
99%
5 steps
59%
77%
90%
95%
10 steps
35%
60%
82%
90%
20 steps
12%
36%
67%
82%
A 95% per-step rate sounds excellent and fails four times in ten on a ten-step task.
Real steps are not independent, so retries push results above the table and correlated failures push them below. What it does tell you is where the effort belongs: moving per-step reliability from 95% to 98% beats any amount of prompt polishing, and you get there through narrower tool contracts, stricter output schemas and fewer steps.
The four failure classes we design against
MAST, the multi-agent failure taxonomy from UC Berkeley’s Sky Computing Lab, analyzed over 1,600 execution traces across seven agent frameworks and grouped 14 failure modes into three categories. Their finding: most failures came from system design, not the model. We design against those three plus a fourth that appears once an agent touches a system of record.
In production
Loops, repeats a done step, never recognizes the task is finished
The control
Explicit termination conditions, step budgets, loop detection
If you have a pilot that works in a demo and stalls before production, that is a five-day diagnosis, not a rebuild.
Book A Stalled Pilot Review
How Much Autonomy an Agent Should Have
Autonomy is a property of each action, not of the agent. Fully autonomous agents are rarer in production than the category suggests, because the same agent can read a payer policy unsupervised and still require approval to submit an appeal. We classify every tool by how expensive its action is to reverse, before writing the orchestration.
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Level 1
Observe
The agent may
Read, retrieve, queryHuman role
None -
Level 2
Recommend
The agent may
Rank and suggest, with reasoningHuman role
Decides and acts -
Level 3
Draft
The agent may
Produce a complete artifactHuman role
Reviews and releases -
Level 4
Execute reversible
The agent may
Act where an undo path existsHuman role
Post-hoc review -
Level 5
Execute irreversible
The agent may
Act with external or financial effectHuman role
Confirms before execution, always
Level 5 is the boundary that gets skipped, and skipping it is how a pilot becomes an incident. We gate it by default. Every level 4 and 5 tool also ships with a dry-run mode that returns what would happen without doing it. Agents start at level 3 and move up only where measured agreement with reviewers justifies it, per action type.
Multi-Agent System and Agent Orchestration
A multi-agent system splits work across specialized agents with an orchestration layer routing tasks and holding shared context. It is the right architecture less often than the market suggests, because splitting one agent into four multiplies the coordination surface.
One agent is enough when the tools are coherent, the task fits the context budget, and one person owns it. Split when at least two of these hold.
The tool sets are disjoint
Tool selection accuracy degrades as the catalog grows
Steps need different models or latency
A real-time scribe and an overnight coding pass optimize for opposite things
Steps sit at different authorization levels
An isolated level 5 agent is far easier to audit
Steps need independent retuning
Isolation is operational, not only architectural
Sully AI is the clearest example of the last row: six agents, each in its own container, so one can be retuned without regression testing the platform.
Orchestration patterns
Pattern
Router, one classifier feeding several specialists
Supervisor, one coordinator delegating and assembling
Sequential pipeline, fixed handoff order
Shared-state collaboration, agents coordinating through a common store
Breaks at
Tasks that span two specialists mid-flight
The coordinator becomes the bottleneck and the point of context loss
Anything needing a step revisited
Concurrency and stale reads, unless versioning is explicit
State coherence is the failure common to all four. Agents acting on stale or divergent views produce behavior that looks like a reasoning failure and gets misdiagnosed as a model problem. The fix is one authoritative state store, versioned reads, and a decision trace recording intent, context and outcome per step.
Tool Use, Funcation Calling and MCP Integration
An agent’s ceiling is set by its tools, not its model. Everything an agent can do in your business is a tool someone designed, described and authorized, which is why most agent quality problems that look like reasoning problems are tool design problems.
Tool contracts
Narrow purpose, typed arguments, and errors that explain how to recover
Permission scoping
Least privilege per agent. No standing write access to irreversible actions
Dry-run modes
Every consequential tool returns what it would do without doing it
Structured output and validation
The schema constrains the output. A separate step confirms it
MCP servers
Your systems exposed once, consumable by any compliant client
Agent interoperability
A2A, where your agents must coordinate with agents you do not operate
Direct function calling
Where a protocol layer would be overhead
MCP connects an agent to tools. A2A connects an agent to other agents, including ones your vendors run. They solve different problems and are not alternatives. MCP is worth adopting when a tool catalog will be shared across several agents or clients, or when you want the tool layer to outlive your current framework choice. It reduces integration rework, not integration work, and it does not solve authorization an MCP server exposes capability, so treat it as a privileged API surface with scoped credentials and full call logging. A2A is worth designing for at the boundary of your organization and rarely worth introducing inside it, where a shared state store is more debuggable than a message protocol.
Agent, Workflow Automation or Chatbot
Most requests that arrive as “we need an agent” resolve to one of the other two once the workflow is mapped. An agent plans its own sequence, calls tools, writes to systems and carries state across steps. A system missing any of those is something else, and often something better suited to the job.
Decides the sequence
Writes to systems
Behavior
Fails by
Cost per run
Right when
Workflow automation
No, fixed at design time
To fixed endpoints
Deterministic
Stopping at a step
Near zero after build
The steps rarely change
AI agent
Yes, chosen per task
Chooses which and when
Bounded, not deterministic
Taking a plausible wrong action
Scales with steps and retries
The path depends on the input
Chatbot
No, turn by turn
Rarely, usually read-only
Bounded, not deterministic
Giving a plausible wrong answer
Low and roughly fixed
The user needs an answer
If you can draw the workflow without a “depends” box, you do not need an agent, and an agent will be slower, costlier and harder to audit than the flowchart. Fixed sequences belong in AI Process Automation.
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Outcome
Did the task complete correctly, against a benchmark set from your real cases with expected results agreed by your domain experts -
Trajectory
Was the path defensible. Right tools, sensible order, no redundant work, terminated when it should -
Tool call
Selection accuracy, argument correctness, error recovery, scored per tool. Where the real defect usually is -
Adversarial
Behavior on inputs it should refuse, retrieved content containing instructions, and cases where escalating is correct
All four run as a regression suite in CI, so a prompt change, a model swap or a new tool triggers the full set before anything reaches production.
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Task completion rate
Single-step accuracy hides how failure compounds across a chain -
Escalation rate and trend
Falling escalation with flat completion means the agent has started guessing -
Cost per completed task
Calculated on completions, not attempts, so retry storms are visible -
Unrecoverable action rate
The only number measuring the risk the agent introduces. Reported to whoever owns the process
AI Agent Development Cost and Timeline
A scoped agent proof of concept typically comes in under $25,000 and runs 6 to 10 weeks. A production agent system, integrated into systems of record with authorization gates, observability and an evaluation harness, generally lands between $30,000 and $100,000 over 4 to 8 months. Multi-agent programs in regulated environments run above $100,000.
Cost driver
What reduces it
Integration surface. Each system needs a tool contract, auth, error semantics and a test double
Start with the two systems carrying most of the workflow, not all seven
Authorization depth. Irreversible actions need confirmation UI and audit trails. Product work, not model work
Ship at draft level first, raise autonomy on measured evidence
Data access, not data volume. Waiting on a system nobody owns is the most common schedule risk
Name a data owner per system before kickoff
Evaluation ground truth. Someone senior defines correct outcomes on a few hundred real cases
Scope domain-expert time explicitly rather than assuming it is free
Regulatory evidence. Risk classification, impact assessment, oversight design
Classify at stage 1, not at go-live
Running cost behaves differently from a model. Inference scales with steps and retries, so a reliability problem shows up as a budget problem first. Budget 15 to 20% of build cost annually. Full bands across all engagement types are on AI development services.
Cost depends on your integration surface, not on the agent. Two weeks of discovery replaces the range with a fixed number.
Get An Estimate For Agent Use Case
The Agent Stack We Build On
Model choice is the least consequential decision here. We architect for portability so a model can be swapped without rebuilding around it. The orchestration, tool, state and evaluation layers are what you actually own.
Agent orchestration
Enterprise agent platforms
Frontier models
Open-weight models
Tools and interoperability
Agent-to-agent protocols
Sandboxed execution
Browser and computer use
Retrieval as a tool
Vector stores
Reasoning and planning
State and memory
Workflow durability
Evaluation
Observability and tracing
Guardrails and safety
Identity and access
Model gateways and cost control
Runtime and infrastructure
Where the agent reasons over your own documents, retrieval is a tool it calls rather than a separate system, and retrieval quality is scored separately from generation quality. That work sits under RAG Development.
Why Teams Choose Us For Agent Builds
01
Authorization designed before the prompt
Every tool classified by reversibility before orchestration is written. What the agent may do is answered in architecture, not discovered in production.
02
The evaluation harness transfers to you
Benchmark set, trajectory tests, tool-call assertions, CI suite. Your team verifies quality after we leave without rebuilding the measurement apparatus.
03
Integration engineers, not only model engineers
A 300+ person engineering organization with fifteen years of systems integration behind it. Agent projects fail at the seam where the agent meets the system of record.
04
ISO 42001 applied to the agent
Risk classification, impact assessment, documented oversight and post-launch monitoring, per system.
05
You own all of it from day one
Source code, tool definitions, prompts, pipelines, harnesses, documentation. Your data never trains models for anyone else.
Start with a feasibility assessment.
Two weeks, at no cost. We review one agent use case against your actual workflow and data, classify every action it would take, and return a written recommendation with architecture options and a fixed estimate. Sometimes the recommendation is that a deterministic workflow would serve you better. You get that in writing too.
Already have an agent that will not ship?
Five working days. We audit orchestration, tool design, evaluation coverage, authorization model and integration surface, and return a findings document naming what is missing and what it would take. Most stalled pilots are missing evaluation and error handling rather than a better model.
AI AGENT DEVELOPMENT FAQS
Explore answers to common questions about MindAI and our AI engineering capabilities.
A workflow executes a sequence decided at design time. An agent decides the sequence at run time based on what the input turns out to be. Workflows are deterministic, cheaper per run and auditable by reading the code. If your process has no genuine “it depends” step, a workflow is the better system and will outperform the agent.
One agent is usually enough. Split when the tool sets are genuinely disjoint, when steps need different models or latency budgets, when steps sit at different authorization levels, or when parts need independent retuning and evaluation. Splitting adds coordination surface, and coordination failures account for roughly a third of documented multi-agent failures in published research.
Three cases, and we turn down projects for all three. When the workflow has no branching, a deterministic pipeline is cheaper, faster, and auditable by inspection, so adding a model adds cost and variance for nothing. When nobody owns the metric the agent is meant to move, it gets judged on impressions rather than results and loses that argument by month four. And when an irreversible action has to happen at machine speed with no confirmation and severe consequences, the workflow is not ready for an agent yet, which is an architecture and process question before it is an AI question.
That depends on whether the completed steps can be undone. Each task is designed with an explicit failure path: retry with corrected arguments where the error is recoverable, compensating actions to reverse completed reversible steps, and escalation to a named human queue with the full decision trace attached where it is not. An agent that fails and simply stops, leaving a record half-updated, is the outcome the design exists to prevent.
Explicit termination conditions, a hard cap on tool calls per task, loop detection on repeated states, and a step budget that escalates to a human when exceeded. Not recognizing when a task is complete, and repeating steps already done, are two of the most common documented agent failure modes, and both are design problems rather than model problems.
The Model Context Protocol is an open standard for exposing tools and data sources to models through a consistent interface, so the same tool layer works across frameworks and models. You need it when a tool catalog will be shared across several agents or clients, or when you want the tool layer to outlive your current framework choice. A single agent calling three internal APIs does not. MCP handles capability, not authorization: which agent may call which tool under whose identity is still your design.
Sometimes, and the answer changes the cost of the project significantly. Options in descending order of reliability: a database or file-level integration behind the application, a middleware layer, a vendor integration partner, or browser-based interaction as a last resort. Browser automation against a system you do not control is brittle and is not something we recommend for an irreversible action. This is normally the first thing we check in discovery.
Chosen per engagement, not per preference. LangGraph where control flow needs to be explicit, stateful and auditable, which covers most regulated work. CrewAI and AutoGen where the pattern is genuinely collaborative multi-agent. Vendor SDKs where the deployment is already committed to one cloud. The framework is the most replaceable part of the system, and we architect so it can be replaced.
Four numbers, tracked from the first sprint rather than reconstructed later: task completion rate end to end, escalation rate and its trend, cost per completed task calculated on successful completions rather than attempts, and the rate of irreversible actions that needed manual correction afterward. Single-step accuracy is the metric that looks best and tells you least.
Running cost scales with steps and retries rather than with users, which is why a reliability problem shows up as a budget problem before a quality complaint. An agent that retries three times costs roughly three times as much for the same output. We instrument cost per completed task from the proof of concept, so the production business case rests on measured numbers.
Your organization, which is precisely why the authorization model matters. Accountability does not transfer to a vendor or to a model. What the architecture can do is make every decision reconstructable: which tools were called with which arguments, what was retrieved, what the agent concluded, who approved it, and when.
Longer than most timelines assume, and it should be earned per action type rather than granted per system. An agent starts at draft level, accumulates human-reviewed decisions, and moves up only where measured agreement justifies it for that specific action. Reversible actions typically graduate within a few months. Irreversible external actions frequently should not graduate at all.
An assistant works alongside a person and waits to be asked. An agent is given a goal and works until it is done or escalates. The distinction is not conversational quality, it is whether the system holds responsibility for finishing a task. Most requests for AI assistant development describe an assistant embedded in software the user already works in, which is a different build with a different permission model. Where that is what you need, see AI Copilot Development.
Sometimes, and usually the better answer is to keep them. RPA is more reliable and far cheaper than an agent on steps that never vary, so the pattern that works is an agent handling the exception paths and invoking existing bots as tools for the fixed ones. Replacing a working bot with an agent trades determinism for flexibility you may not need. Rule-based and high-volume fixed automation is covered under AI Process Automation.
Usually, and it is often the faster route. Existing platforms already hold the integrations, the auth and the audit trail, so the agent adds reasoning and tool selection rather than replacing what works. The question to answer first is whether the platform lets you observe and gate individual tool calls. Where it does not, you get an agent you cannot debug or govern.
Looking for other Services?
Explore our other related services to enhance the performance of your digital product
AI Process Automation
For deterministic, high-volume workflows where the sequence is known
For connecting AI into ERP, EHR and claims systems without replacing them
MLOps Consulting
For evaluation, drift monitoring and keeping accuracy from slipping after launch
AI Governance Consulting
For EU AI Act classification, ISO 42001 alignment and decision provenance
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