Teams of AI agents that split up complex work, check each other and hand off to people.
Stack we work in
Figures as of Oct 2026
Trusted by
Some processes are too long or varied for a single AI agent: a claim needs reading, checking against policy, pricing and a letter; a research request needs searching, comparing and writing.
A multi-agent system gives each step to a specialized agent and coordinates them, with clear limits on what each one can do and a person approving the decisions that matter. We build these systems with LangGraph, CrewAI, Microsoft Agent Framework (the successor to AutoGen) and the OpenAI Agents SDK, connect agents to your tools through MCP and to each other through the A2A protocol, and trace every step so you can see why the system did what it did.
A single agent with dozens of tools and rules becomes slow, costly and hard to debug.
Multi-day work with waits, approvals and retries breaks simple agent loops.
Nobody reviews the agent's work before it reaches a customer or system.
Agents from different vendors or teams cannot share work.
When a run fails there is no trace showing which step went wrong.
Outcomes
Agent roles, tools, handoffs, approval points and success metrics, agreed before the build.
The orchestrated system, deployed in your cloud with APIs and an operator interface.
MCP servers and API integrations with scoped credentials and audit logging.
Limits, filters and human approval steps, documented and configurable.
Scenario tests with expected outcomes, rerun before every release.
Dashboards for runs, cost and failures, plus code and documentation handed to you.
Read the case, check policy, gather evidence, draft a decision and send it to a handler for approval.
Search sources, compare findings, write a draft report and have a reviewer agent check citations.
Research accounts, enrich CRM records, draft outreach and schedule follow-ups for review.
Triage incidents, gather logs, suggest fixes and open change requests.
Match documents, chase missing items, flag exceptions and prepare entries for sign-off.
Agents that plan, write, test and review code changes inside your repository rules.
Choose the right shape for the work.
Built with
Let agents use your systems and work with other agents.
Built with
Keep agents inside the limits you set.
Built with
See every step and measure the result.
Built with
Use the right model for each agent.
Built with
Supervisor, hierarchical or pipeline orchestration is chosen after mapping your process, not before. Some processes need one agent, and we say so.
Step-by-step traces in Langfuse, LangSmith or Arize Phoenix, with replay of failed runs so you can see which agent went wrong and why.
Per-agent permissions, approval steps for payments and record changes, and step, time and cost limits stop runaway loops.
MCP and A2A keep agents interoperable, and each role gets the model that fits it through one gateway, so you are not tied to one framework or vendor.
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
We map the process step by step, the systems involved and where people must approve.
You receive Process map and agent roles
We choose the orchestration pattern, framework, models and guardrails, and define success metrics.
You receive Architecture and test scenarios
A working system handles real cases end to end in a sandbox, with traces for review.
You receive Working prototype
We connect agents to your systems through MCP and APIs, add approvals and limits.
You receive Integrated system
The system runs on real work alongside your team while we measure success and failure.
You receive Pilot results
We monitor runs, review failures and tune agents, prompts and tools over time.
You receive Monitoring and improvement plan
Tech stack
AI and machine learning
Databases
Backend and APIs
Languages
Cloud and DevOps
Other tools
Next step
If your process fits a single agent with a few tools, a simpler build is faster and cheaper.
It depends on the number of agents and steps, how many systems they connect to, the approval and audit rules, the volume of work and the level of testing the risk calls for. Model usage is estimated separately and can be capped. As a guide, a multi-agent pilot on one process usually costs $20,000 to $50,000, and a production system with integrations, guardrails and monitoring $60,000 to $200,000 or more (USD).
One agent is enough for most tasks with a handful of tools. Several agents help when the process has distinct stages with different skills, when one agent's prompt and toolset would become too large to manage, or when you want one agent to check another's work.
LangGraph when we need precise control over state and branching, CrewAI for role-based research and content teams, Microsoft Agent Framework for Microsoft-centered clients, and the OpenAI or Claude Agent SDKs for simpler handoff patterns. We choose per project and keep business logic portable.
MCP (Model Context Protocol) is an open standard for connecting agents to tools and data. A2A (Agent2Agent) is an open protocol for agents to discover and call each other, even across frameworks and companies. Using both keeps your system open to new models and vendors.
Each agent only gets the tools and data it needs, risky actions wait for human approval, every run has step, time and budget limits, and inputs and outputs pass through guardrails. We test against misuse and prompt injection before launch.
Yes. Every run is traced: which agent acted, what it read, which tool it called, what it returned and what it cost. Traces can be searched, replayed and exported for audit.
Yes. We connect to CRM, ERP, ticketing, email, document stores and databases through MCP servers or APIs, and start in read-only mode before allowing any writes.
They can be if every agent uses the largest model. We use smaller models for routine steps, cache repeated work and set per-run budgets, and report cost per completed task.
A prototype on one process usually takes 4 to 8 weeks. A production system with integrations, guardrails and evaluation typically takes 3 to 6 months, delivered in stages.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. We need a map of the process, the systems each step touches, and examples of completed work. A one-week design sprint produces the agent roles, tool list and a written estimate.
You own the code, prompts, agent definitions and traces. Data stays in your systems and your chosen model accounts, under NDA. After launch we monitor task success, cost and failures, and can run the system under a support agreement.
Book a call. We will map the process with you and show where a team of agents fits and where people stay in control.
