Build multi-agent systems that plan their own steps across long-running business processes and carry context forward between every run and handoff
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Our agentic AI development services help you move from a single automated task to a system of agents that runs the whole process
Designing the agent roles and the orchestration between them, then building the shared memory and tool access each process needs to run unattended
Get advice on which business processes can be delegated to agents and how much autonomy each step safely carries under your existing security controls
We connect agents to your APIs and internal data, configure the guardrails and approval points, then run them alongside your team in production
Building a working agent chain around one real production process within weeks, so you can watch it operate before committing the complete budget
Get an architecture where a supervisor agent assigns each step and memory carries context from one run to the next
Set how far each step acts alone, from suggestion to full autonomy, and change the level without a redeploy
| LEVEL | WHAT THE AGENT DOES | YOUR TEAM |
|---|---|---|
| Suggest only | Drafts the action and stops. Nothing reaches your systems | Reviews and executes |
| Act with approval | Prepares the action and holds it at the gate until someone releases it | Approves each action |
| Act and report | Completes the step, then writes what it did and why into the run log | Audits afterwards |
| Act autonomously | Runs the step start to finish. No notification, full trace on demand | Reads the trace |
| LEVEL & WHAT THE AGENT DOES | YOUR TEAM |
|---|---|
| Suggest only Drafts the action and stops. Nothing reaches your systems | Reviews and executes |
| Act with approval Prepares the action and holds it at the gate until someone releases it | Approves each action |
| Act and report Completes the step, then writes what it did and why into the run log | Audits afterwards |
| Act autonomously Runs the step start to finish. No notification, full trace on demand | Reads the trace |
| LEVEL & WHAT THE AGENT DOES |
|---|
| Suggest only Drafts the action and stops. Nothing reaches your systems YOUR TEAMReviews and executes |
| Act with approval Prepares the action and holds it at the gate until someone releases it YOUR TEAMApproves each action |
| Act and report Completes the step, then writes what it did and why into the run log YOUR TEAMAudits afterwards |
| Act autonomously Runs the step start to finish. No notification, full trace on demand YOUR TEAMReads the trace |
We build agentic AI solutions that enable businesses across various industries to redefine workflows, automate complex tasks, and boost overall efficiency
Coding and policy agents share a single case file and keep reopening appeals until reimbursement finally closes.
Agents hold a patient's full history across every visit and revise the care plan when new results contradict it.
A scheduling agent negotiates clinician time with a documentation agent, then rebooks as a slot appears.
A triage agent ranks the urgency while a records agent pulls the prior history, then the two pick a specialist
A monitoring agent opens the case and an evidence agent assembles context until the two agree to freeze funds.
Agents keep each client's stated goals in long-term memory and re-plan whenever the markets move against them.
Agents request the missing documents and re-score the file each time one arrives, without any fixed rule tree.
A watcher agent tracks the regulatory changes and briefs the agents that own each of the controls it can affect.
A tutor agent carries a learner model across terms while a planner agent adjusts the path when mastery stalls.
A grading agent scores the work and passes the weak areas to a tutor agent that rewrites the next assignment set.
A monitoring agent flags a disengaging cohort and a delivery agent reworks the next session around its own gaps
Agents compare the outcome data against standards each term and draft the revision for the faculty to approve.
An agent recalls a returning buyer's fit and budget, then negotiates stock and delivery with fulfillment agents.
A forecasting agent hands a shortfall to a purchasing agent that places the order and then chases the supplier.
Pricing agents test a change, watch conversion and margin for a full day, then revert or hold it on their own.
A returns agent books the carrier pickup and a finance agent settles the refund under the spending limit you set
When a flight cancels, agents rebuild the itinerary and rebook through supplier APIs before the traveler asks.
A demand agent and an inventory agent argue over each rate change against the revenue target that you have set.
A guest agent takes the request and dispatches an operations agent, then follows up until the guest confirms it.
Housekeeping and maintenance agents reshuffle their own schedules whenever a late checkout disrupts the daily plan.
What changes when a system of agents owns a whole process instead of a single bounded task
80%
Autonomous AI agents remove the waiting time between steps, so a request that sat in a queue between each handoff now moves straight through
50%
Agentic AI solutions automate repetitive tasks and simplify business processes, enabling teams to focus on strategic, high-value work
31%
Productivity improvement reported by organizations that redesign their operating model around agentic AI, alongside a 27% gain in efficiency through shorter cycle times and fewer errors
Cleveroad’s engineers employ the best practices for agentic AI development, focusing on your business success and smooth workflow automation
Our engineers build supervisor and worker agent topologies and wire tool access through your existing APIs and data pipelines. We pick the orchestration framework after the process is mapped rather than before a single agent is written.
Our AI engineers have shipped in 9 business domains, including the ones adopting agentic systems fastest: healthcare, fintech, education, and ecommerce. We design agent systems around each domain's data rules and regulatory limits.
We cover the lifecycle from use case discovery and architecture through agent development, integration, testing, and post-deployment support. That support includes agent evaluation and policy updates as your processes change, alongside uptime monitoring.
We connect agents to your CRM and ERP systems with scoped permissions, so each agent acts only where you allow it and nowhere else. Existing systems stay in place, and nothing gets rebuilt or replaced to accommodate the new agent layer.
We follow a proven agentic AI development life cycle to deliver scalable agentic AI systems tailored to your business needs and technical landscape
The process begins with mapping your operations to find where a system of agents can own the whole flow rather than a single step. You get a shortlist ranked by feasibility, data readiness, integration effort, and the autonomy each process safely carries.
Key outcomes:
Candidate process shortlist
Data readiness assessment
Autonomy and risk review
Delivery roadmap
Together, we define the agent roles and the orchestration between them, then design the memory each agent reads from and the permissions it acts under. Guardrails and approval points are set here, before any code is written.
Key outcomes:
Agent role map
Orchestration topology
Memory and retrieval design
Guardrail and approval rules
A working agent chain runs on one real process against your data, deployed in cloud or on-premises to suit your security review. You watch the agents hand work to each other and recover from failures before committing to full development.
Key outcomes:
Working agent chain
Live process run
Evaluation baseline
Go or no-go decision
The full agent set connects to your CRM and ERP through APIs and data pipelines. Exception handling and retry paths cover every step where an agent can fail, and security review runs in parallel with the build.
Key outcomes:
Full agent set
System integrations
Exception and retry paths
Security sign-off
Each agent is scored against a test set, and run tracing switches on before the first live case. Autonomy levels then rise one step at a time, so your team sees how the system behaves before it acts alone.
Key outcomes:
Run tracing setup
Regression test suite
Autonomy level tuning
Support handover
We help our clients identify and develop agentic AI solutions that align with their business goals and improve daily workflows
Germany
Healthcare
Challenges solved during the creation of an agentic claims system:
United Kingdom
Fintech
Challenges solved during the creation of an agentic credit scoring:
Learn about Cleveroad’s expertise in Projects Portfolio
in Projects Portfolio
Show moreWe keep deepening our expertise to meet your highest expectations and build business innovative products
ISO 27001
Information Security Management System
ISO 9001
Quality Management Systems
AWS
Select Partner Tier
AWS
Solutions Architect, Associate
Scrum Alliance
Advanced Certified Scrum Product Owner
AWS
SysOps Administrator, Associate
DenmarkCTPO of Penneo A/S
"Cleveroad proved to be a reliable partner in helping augment our internal team with skilled technical specialists in cloud infrastructure."
The orchestration and memory layers we build on, and the runtimes we deploy agent systems to
We offer flexible engagement options from a classic human team to AI-native squad. Pick the model that matches your scope and budget
The dependable, human-led baseline suited to any kind of project.
Development budget
Team composition
Iterative delivery
Spec evolves during delivery
Write a sprint scope, deliver it, try it, then revise the spec or functionality.
Best for
Flexible-scope projects where each iteration reshapes the plan as you go
Your familiar full-stack squad, accelerated with AI support in every role.
Development budget
Team composition
Iterative delivery
Spec evolves during delivery
The same write-deliver-review sprint repeats, just accelerated with AI.
Best for
Brownfield projects that need fast iteration without locking scope too early
A minimal lean team with maximum AI leverage and strict human oversight.
Development budget
Team composition
AI Solution Architect. Owns the approved spec and the architecture the build follows
Prompt Engineer. Turns the spec into precise prompts and guardrails for AI code generation
AI Product Owner. Owns milestones and acceptance against the fixed spec
Milestone delivery
Spec locked before delivery
Approve once upfront. The spec becomes the source of truth for milestones.
Best for
MVPs and greenfield projects with fixed scope and milestone-driven delivery
70 clutch reviews
4.9
Award
Clutch 1000 Service Providers, 2024 Global
Award
Clutch Spring Award, 2025 Global
Ranking
Top AI Company,
2025 Award
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Top Software Developers, 2025 Award
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Top Web Developers, 2025 Award
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Top Staff Augmentation Company in USA, 2025 Award
