Agentic AI
Agentic AI systems that fail loudly, not quietly
We design and build multi-agent systems that plan, call tools and act on your data inside the workflows you already run, with the guardrails, evaluation and tracing that let an operations team trust them after launch.
Technology we use for this work
The problem
Most agent demos never survive their first month
An agent that works in a demo is easy. An agent that behaves the same way on the ten thousandth run, stays inside a budget, refuses what it should refuse and leaves a record an auditor can follow is an engineering problem.
The failure modes are predictable: unbounded loops, tools with loose contracts, silent partial failures, no way to replay a bad run, and no measure of whether a prompt change made things better or worse. We design against each of them from the first commit.
What we build
What does our agentic AI service include?
Planner and worker agents with typed tools, bounded retries, cost ceilings and full tracing.
Planner and worker architectures
Agents with narrow responsibilities and explicit hand-offs, orchestrated with LangGraph or plain code when a framework would add more failure modes than it removes.
Typed tool contracts
Every tool an agent can call has a validated input and output schema. Malformed calls are rejected before they touch a system, not discovered in a log afterwards.
Bounded retries and circuit breakers
Stage timeouts, retries with backoff and jitter, then a circuit breaker. Retry policy belongs to the runner, never to an individual step.
Per-run cost ceilings
Every run carries a budget. Spend is recorded per request and per stage, so finance sees model cost before the invoice arrives.
Human-in-the-loop checkpoints
Approval steps where a decision has consequences: drafting is automated, sending, filing or writing to a system of record waits for a person.
Evaluation before claims
A held-out task set agreed with you before the build, regression runs on every prompt and model change, and LLM-as-judge scoring with Ragas or DeepEval where it helps.
Tracing and replay
Every model call and tool call is traced against the prompt version that produced it, in Langfuse, LangSmith or Arize, so a bad answer can be reproduced and fixed rather than argued about.
Model routing across providers
One gateway in front of Anthropic, OpenAI, AWS Bedrock, Bunyan, Groq and OpenRouter, so each step uses the model that fits its cost, speed and accuracy needs, and switching provider is a configuration change rather than a rewrite.
Workflow automation
Agents placed inside the workflows your team already runs, built in n8n, Make or Dify. A ticket, email or form submission triggers the agent, and its output lands in your CRM, inbox or approval queue rather than in a separate chat window.
Knowledge graph reasoning
Agents that traverse entities and relationships in a graph instead of guessing across flat tables, including agents that generate graph queries and charts programmatically.
Architecture
How does it work, step by step?
-
Request
Intent and routing
- Classify the task
- Resolve references to earlier turns
- Pick the path, or refuse
-
Plan
Planner agent
- Break the task into steps
- Choose typed tools
- Attach a run budget
-
Act
Worker agents
- Retrieval, query, analysis
- Validated tool calls
- Timeouts and circuit breaker
-
Check
Guardrails and review
- Evaluation checks
- Human approval where it matters
- Structured errors, never silent
-
Record
Trace and report
- Every call traced
- Cost per run
- Replayable input
- Read-only access unless a write path is explicitly designed and approved
- No implicit defaults: every call names its data source, so a misrouted request fails instead of answering from the wrong place
- Prompts versioned outside the codebase and traced per call
- Dead-letter records that carry the input needed to reproduce a failure
- Model access through one gateway, so the model can change without the application changing
Proof
Where have we delivered this?
Multi-agent retail intelligence on a knowledge graph
A multi-agent system that answers business questions, forecasts and draws its own charts by reasoning over a retail property graph.
Result: 4 specialized agents over one governed graph
Read the case studyAn AI equity research platform that cites every claim
Enter the ticker of a US-listed company and the platform reads five years of filings, earnings call transcripts and market data, then writes a cited memo with growth signals.
Result: 100 names tracked with the same depth every quarter
Read the case studyAn AI compliance platform for Saudi PDPL, live with banks
A platform that reviews client documents against the Saudi Personal Data Protection Law, scores the gaps and drafts compliant replacements, now in production with banks and finance companies.
Result: 95% content fidelity on AI-drafted documents
Read the case studyContext
Industries and related services
FAQ
Agentic AI: common questions
What makes an agentic system production-ready?
Four things we check before launch: every tool has a typed contract, every run has a budget and a timeout, quality is measured against an agreed task set on every change, and every model and tool call is traced so a failure can be replayed. Without those, an agent is a demo.
How do you stop agents from taking harmful actions?
Agents get the narrowest permissions the task needs, usually read-only. Actions with consequences, such as sending, filing or writing to a system of record, go through a human approval step. Write operations that are not designed in are rejected by the system, not by a prompt instruction.
Do you use a framework like LangGraph or build from scratch?
Both, depending on the job. LangGraph and LangChain Deep Agents suit stateful multi-step workflows, and we also build with CrewAI, AutoGen and the OpenAI Agents SDK when a team has already standardized on one. For simpler pipelines, plain code with a clear runner is easier to test and operate. We choose per system and explain the trade-off in the architecture document.
Which models do you build on?
Anthropic Claude and OpenAI models, models on AWS Bedrock, Bunyan, and fast inference through Groq and OpenRouter, plus open-weight models served with vLLM or Ollama when data must stay in your environment. Every system routes model calls through one gateway with a registry behind it, so you can change models for cost, accuracy or residency reasons without rewriting the application.
Can you connect agents to our existing workflow tools?
Yes. We build agentic workflows in n8n, Make and Dify when your operations team wants to see and adjust the steps themselves, and in code when the workflow needs versioned tests and tighter control. Either way, the agent sits behind the same guardrails: typed tools, run budgets, human approval for consequential actions and full tracing.
How long does an agentic AI project take?
Every engagement starts with a two to three week fixed-scope assessment that tests one hard task end to end and prices the real work. Build timelines depend on the number of tools, data sources and approval steps, and we commit to them after the assessment, not before.
Start with a two to three week assessment.
Send us the problem and the constraints. You get a written architecture, a risk register and a costed roadmap, and you keep them whatever you decide next.