Agent Architecture
Planner, specialist agents, the tool access, the memory and the budget on paper.
AI Agents
Multi-Agent SystemsSpecialist Agents, One Orchestrator.
Multi-agent systems for problems that need more than one agent — specialist agents co-ordinated by a planner with a budget, with the tool access, the memory, the guardrails and the evaluation suite that make the system safe enough to ship to a customer.
10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served
Agent Architecture
Specialist Agent Build
Orchestrator & Planner
Tool & API Access
Memory & State
Guardrails & Evaluation
Overview
A multi-agent system is a team, not a chain
A multi-agent system is a team of specialist agents, co-ordinated by a planner with a budget. Each agent has its own role, its own tools and its own memory. The planner decides which agent does what, when, and how much it can spend. The system is the team, not a chain of prompts.
We build multi-agent systems with the planner, the specialist agents, the tool access, the memory, the guardrails and the evaluation suite as part of the architecture from sprint one. The output is a system that ships, not a demo that lives in a notebook.
This is the wrong engagement if the use case can be solved by a single agent or a single prompt. A multi-agent system is a serious investment, and the right answer for a one-off task is a simpler architecture.
- Team, Not Chain — A team of specialist agents, not a chain of prompts, with the planner as the orchestrator.
- Bounded by Budget — Each agent has a budget, with the spend tracked and the over-spend caught early.
- Evaluated, Not Vibes — An evaluation suite that runs on every change, with the regressions caught early.
- Audit-Logged — Every action stamped with the agent, the inputs, the outputs and the user.
What we deliver
Everything included in our multi-agent systems
Specialist Agent Build
The specialist agents — sales, support, research, operations — with the role and the tools.
Orchestrator & Planner
The planner that decides which agent does what, when, and how much it can spend.
Tool & API Access
The tool layer — APIs, databases, internal systems — that the agents call.
Memory & State
Short-term and long-term memory, with the storage and the retrieval the agents need.
Guardrails & Evaluation
The safety filters, the budget, the audit log and the eval suite the system needs.
Our process
A proven process for successful delivery
01
Discover
We agree the use case, the agents, the tools and the architecture on paper.
02
Plan & Design
We design the planner, the agents, the tools and the evaluation suite.
03
Develop
We build in two-week sprints with a working slice every Friday.
04
Deploy
We ship to production with the guardrails, the budget and the observability live.
05
Optimize & Grow
We read the data, the cost and the evaluations, and ship the next iteration.
Technology
Built with a stack that stays maintainable
Agent Frameworks
- LangGraph
- CrewAI
- AutoGen
- Custom
Models
- OpenAI
- Anthropic
- Google Gemini
- Open source LLMs
Tooling
- LangChain
- Custom tool layer
- Internal APIs
Observability
- LangSmith
- Helicone
- OpenTelemetry
What you can expect
- To First Production Release
- 8-14 wksTo First Production Release
- Target Uptime
- 99.9%Target Uptime
- Actions Audit-Logged
- 100%Actions Audit-Logged
- Budget per Agent
- BoundedBudget per Agent
Industries we serve
Delivered for teams in your sector
When the use case has more than one role, more than one tool, or more than one kind of decision. A single agent with a long prompt is the wrong answer for that — the prompt becomes brittle, the evaluation becomes hard, and the cost becomes unpredictable. A multi-agent system is the right answer when the work is naturally a team.
A budget per agent, a budget per request, and a budget per user. The spend is tracked, the over-spend is caught early, and the agent stops when the budget is exhausted. The cost is a non-functional requirement, not an afterthought, and the dashboards show the per-request cost against the budget.
The evaluation suite includes the per-agent evals and the end-to-end eval. The per-agent evals catch the regressions inside an agent. The end-to-end eval catches the regressions in the orchestration. Both run on every change, and the result is in the deploy log.
Every action is logged with the agent, the inputs, the outputs, the user and the timestamp. The log is queryable, exportable and retained for the period the compliance review needs. The audit log is part of the architecture, not a wrapper, and the dashboards surface the actions the team can act on.
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Ready to start your multi-agent systems project?Let’s scope it together.
Tell us the outcome you need. We’ll come back with an approach, a timeline and a written estimate.