Agents that run your business.
Not demos. Production.
We design and build multi-agent systems — orchestrated AI architectures where specialized agents reason, plan, delegate, and act autonomously. Built on MCP and A2A protocols. Deployed to production. Governed from day one.
MCPA2AReActLangChainLlamaIndexPineconeKubernetes
An agent is not a chatbot. A multi-agent system is not a pipeline.
Most things marketed as “AI agents” are chatbots with a tool call or two attached. An agentic system is a different class of software — one where AI is not a feature but the reasoning and execution layer.
A genuine agentic system has four properties:
Autonomous goal pursuit.
Given an objective, the system breaks it into sub-tasks, determines how to accomplish them, and executes — without being told the exact steps. The human defines the what. The agents determine the how.
Tool use and environment interaction.
Agents call APIs, read databases, write files, send messages, trigger workflows, and use other agents as tools. The boundary between the AI system and the operational environment is intentionally porous.
Multi-step planning with state.
Complex tasks require holding context across many steps — remembering what was done, what failed, the current state, and what comes next. Agentic systems maintain this across a planning and execution loop.
Adaptive recovery.
When a step fails — an API is down, a result is unexpected, a constraint is violated — the system recovers and retries rather than failing silently or requiring human intervention for every edge case.
What multi-agent architecture adds
When the task is complex enough that a single agent reasoning end-to-end produces unreliable results, you distribute the work. A multi-agent architecture assigns specialized roles — planner, researcher, writer, validator, executor — and coordinates their work through an orchestration layer. Each agent does what it’s good at. The orchestrator ensures the outcome is coherent. This is not a philosophical position. It is a practical architecture decision that produces more reliable, more maintainable, and more observable systems than a single monolithic agent attempting everything.
Built on open standards. Designed to scale.
The difference between an agentic system that scales and one that becomes a maintenance burden is often architecture, not capability. In 2026, the consensus enterprise architecture has converged on three open protocol layers. We build on all three.
MCP — The Tool Access Layer
MCP gives every agent standardized, secure access to the tools, data sources, and APIs it needs. Rather than building custom connectors for every integration, MCP servers are modular, reusable, and model-agnostic. As of 2026, MCP runs on 10,000+ enterprise servers and is adopted by OpenAI, Google, Microsoft, Anthropic, and AWS.
We use MCP as the foundational tool-access layer for every agentic system we build.
A2A — The Agent Coordination Layer
A2A enables independent AI agents — regardless of framework or vendor — to communicate, delegate tasks, and coordinate toward a shared goal. Launched in 2025, it reached v1.0 in early 2026 and is in production at 150+ organizations. It’s what makes multi-agent architectures maintainable.
We use A2A for cross-agent coordination, layered on top of MCP tool access.
Orchestration — The Reasoning & State Layer
Above the protocol layer sits the orchestration logic — where agent roles are defined, planning loops run, state is maintained across multi-step tasks, and recovery logic handles failures. We use LangChain and LlamaIndex depending on RAG and retrieval requirements.
Protocol layers are infrastructure; orchestration is architecture.
ORCHESTRATION LAYER
LangChain / LlamaIndex · ReAct loops · Planning · State · Recovery
A2A LAYER
Agent-to-agent coordination · Task delegation · Role-based access
MCP LAYER
Agent-to-tool connectivity · APIs · Databases · File systems · Actions
Agent requests flow top → down; results flow back up.
This architecture makes your system future-proof. When a new LLM provider offers better performance, you swap the model. When a new tool is needed, you add an MCP server. When a new agent role is needed, you introduce it through A2A. Nothing requires rewriting the system from scratch.
The systems we architect.
Every agentic system we build starts from a clear definition of what work the agents are doing and for whom. Here are the primary system types we design and deploy.
Autonomous Operations Agents
Agent systems that take over operational workflows end-to-end — ingesting inputs, processing through a multi-step reasoning and action loop, and producing outputs or taking actions without per-task human approval.
financial reconciliation, compliance monitoring, supply-chain exception handling, invoice processing, HR intake & triage.
3–6 agents + orchestrator + MCP layer · 12–20 weeks
Research & Intelligence Agents
Multi-agent systems that gather, synthesize, and reason over large, heterogeneous information sources — delivering structured intelligence that would take human analysts days, in minutes.
competitive intelligence, regulatory monitoring, technical due diligence, M&A data-room analysis, patent landscape mapping.
planner + researcher swarm + synthesis agent + RAG · 8–16 weeks
Customer-Facing Autonomous Assistants
Production-grade AI assistants that handle complex, multi-turn customer interactions autonomously — resolving issues, completing transactions, escalating appropriately — without a human in the loop for most interactions.
autonomous support resolution, AI sales assistants, onboarding & qualification, account management.
orchestrator + domain agents + escalation + handoff layer · 10–18 weeks
Internal Workflow Copilots
Agent systems that work alongside human teams — surfacing relevant information, drafting outputs for review, executing sub-tasks, acting as an always-available specialist for specific domains.
engineering copilots, legal document review, sales intelligence, HR policy advisors, code review agents.
context agent + RAG knowledge base + tools + review layer · 6–12 weeks
Multi-Agent Data Pipelines
Orchestrated agent networks that replace brittle, rules-based ETL with AI-driven data processing — handling messy, unstructured, or highly variable inputs that deterministic pipelines break on.
unstructured data normalization, multi-source fusion, content moderation, data validation, KB construction.
ingestion agents + processing swarm + validation + output · 8–14 weeks
Most production agentic systems combine elements of two or more types above. The Architecture Conversation is where we map your specific use case to the right topology.
Every agentic engagement begins with an Architecture Conversation.
Not a discovery call. Not a sales pitch. An Architecture Conversation. We schedule 60 minutes with a senior Kharaayo engineer — someone who has built multi-agent systems, not someone who has read about them.
You bring the problem
What are you trying to automate? What does success look like? What systems does it need to touch? What are the constraints — compliance, latency, cost, reliability?
We bring the architecture
Based on what you describe, we map the system topology: how many agents, what roles, what protocol layers, what infrastructure, what failure modes to design for, what it takes to get from zero to production.
You leave with something concrete
Not a deck. A written architecture sketch — a clear picture of what the system looks like, what it requires, and what the path to production involves. Rough but real. No obligations on either side.
This conversation is free
- For Agentic Systems engagements, we don’t charge for the initial architecture conversation — the system’s requirements need to be genuinely understood before either party commits.
- If what you’re describing is out of our scope, we’ll say so. If it’s the kind of build we’re good at, we’ll tell you what a proposal looks like.
After the Architecture Conversation, for clients who want to proceed, we produce a full technical specification — a paid document detailing the architecture, component specifications, integration requirements, data flows, governance model, cost model, and phased build plan. This becomes the contract for the build engagement.
From conversation to production.
Architecture Conversation60 minutes, free
Described above. You leave with a written architecture sketch. No obligations.
Technical Specification1–2 weeks, fixed fee
A paid document that takes the architecture sketch to full specification: agent roles, inter-agent flows via A2A, MCP server definitions, data models, state management, observability, governance, cost model at scale, and a phased build plan.
This document is the contract for the build. Nothing is ambiguous at Phase 3.
Technical Specification fee: [PLACEHOLDER — confirm with Daniel. Typical range: $5,000–$12,000 depending on system complexity.]
System Build8–24 weeks, scope-dependent
Two-week sprints. At each sprint close: a running build, a written summary, and a cost-vs-plan check. Every sprint includes an agent behavior review — we run the agents against defined test scenarios and you see how they behave, not just whether they compile.
Every decision that changes the specification requires explicit agreement — a written change-log entry, not a Slack message.
Staging, Evaluation & Hardening2–4 weeks
Agentic systems require evaluation beyond standard QA. We run the system against a comprehensive scenario library: happy path, failure modes, edge cases, adversarial inputs, cost-spike scenarios, escalation triggers. Nothing moves to production until it behaves predictably across the library.
Production Deployment & Handoff1–2 weeks
Production deployment on the agreed infrastructure. Observability stack live (traces, cost monitoring, error alerting). Full documentation: architecture diagrams, agent specs, MCP server docs, runbook, incident playbook.
The first two weeks in production are supported — we’re watching the same dashboards you are.
Optional: Ongoing Systems Retainer
Multi-agent systems evolve. New tools need MCP servers. Models improve. New agent roles are added. A monthly retainer keeps a named senior engineer on the system. Month-to-month, cancel anytime.
Agentic Systems engagements are priced on system complexity: number of agents, integration surface, compliance requirements, infrastructure scope. Indicative range: $60,000–$250,000+ for a full multi-agent system design and build. The Technical Specification phase produces the exact fixed-fee proposal. [PLACEHOLDER — confirm pricing guidance with Daniel before launch.]
What breaks agentic systems in production.
Most agentic system failures are architectural, not model-quality failures. Knowing the failure modes before you build is the difference between a system that runs reliably and one that embarrasses you in front of a client.
Compounding Errors
In a multi-step planning loop, a small error in step 2 becomes a large error by step 8. Without a validation checkpoint between agent steps, the system confidently produces a wrong answer at the end of a long correct-looking chain.
Validation agents at defined checkpoints. Step outputs are verified before being passed downstream. The orchestrator knows which steps require validation and which can run freely.
Runaway Agent Loops
Agents in a ReAct loop can get stuck — attempting the same failing action repeatedly, calling tools in a cycle, or pursuing a sub-goal past the point where it’s useful. Without loop detection and cost controls, this becomes expensive and unrecoverable.
Hard loop limits, step-count budgets, LLM call cost caps per task, and anomaly detection that alerts when a task exceeds its expected resource envelope.
Context Window Degradation
Long multi-step tasks fill the context window. As it fills, the agent loses track of earlier constraints, repeats work, or contradicts earlier decisions. Naively passing full context across every step is a scaling anti-pattern.
Structured state management. Each agent step receives only the context it needs. A state object tracks the task’s history outside the context window.
Uncontrolled Tool Surface
Giving agents access to tools they don’t need creates attack surface — from adversarial inputs and from well-intentioned agents making irreversible actions. An agent that can send emails, delete records, and call external APIs is a real operational risk if not scoped.
Principle of least privilege on MCP tool access. Each agent role gets exactly the tools it needs. Write and delete actions require explicit human-in-the-loop approval unless specifically waived.
Governance Blindness
A multi-agent system that runs autonomously without an audit trail is ungovernable. When something goes wrong — and eventually something will — you need to know which agent made which decision, which tool was called with what input, and what the reasoning was.
Full distributed tracing across the agent network. Every action, tool call, and inter-agent message is logged with timestamp, inputs, outputs, and reasoning. The observability stack is designed before the build begins.
Building a production-grade multi-agent system means designing for these failure modes from the architecture phase. They cannot be patched in after launch.
Systems we’ve built.
Agentic architectures we’ve designed, built, and deployed — for our own ventures and for clients.
ProLeap — AI Agent Layer
Two-agent system for career guidance and talent placement
An AI Career Consultant and an AI Placement Manager, in technical specification for integration into the ProLeap platform. The Consultant provides personalized, context-aware career guidance; the Placement Manager automates job matching, outreach, and interview prep.
Architecture: Two-agent system with shared learner context store · LangChain orchestration · RAG over career/job KB · function calling for platform actions
Agenhost
Internal venture
[PLACEHOLDER — Agenhost one-line description from Daniel. Application-layer architecture defined in May, advancing through June. To describe the core agentic architecture when available for public description.]