Define a bounded job
Turn a broad agent idea into a specific user, task, decision boundary and measurable operating outcome.
AI agent development consulting
AI agents built for the work after the demo
From a bounded business task to an evaluated, integrated and operable agent system, with your team able to understand what it inherits.
The result
An agent system your organisation can operate
Design the whole system
Treat models, context, data, tools, state, identity and user experience as one architecture rather than isolated components.
Prove behaviour before scale
Use representative scenarios, traces and evaluations to expose failure modes before the agent reaches wider use.
Leave an operable product
Build deployment, monitoring, documentation and knowledge transfer into the engagement, not into a later rescue phase.
Engagement paths
Start from a valuable scenario or a prototype that needs to grow up
The work can begin before architecture exists or after a prototype has exposed where production becomes difficult.
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New systems
Move from scenario to production path
For teams with a valuable workflow and a clear need, but no settled agent architecture or delivery approach.
- Scenario, user journey and success definition
- Architecture, platform and integration decisions
- Iterative build, evaluation and production handover
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Existing prototypes
Turn a promising demo into a dependable system
For teams that have validated an idea but need help with reliability, security, observability, scale or maintainability.
- Architecture and failure-mode review
- Evaluation, guardrail and tool-control hardening
- Deployment, monitoring and operating model uplift
Built for
The people responsible for the workflow and the system around it
Product and operations teams
Shape an agent around the real work, users and service outcome instead of around a technology demonstration.
Engineering teams
Add specialist agent architecture and delivery capacity while keeping the system understandable and maintainable.
Platform and security teams
Establish identity, access, data, deployment and monitoring boundaries that can survive production use.
Delivery scope
Every layer required between intent and operation
The engagement is tailored to the scenario, but the delivery lens stays end to end.
Discovery and task design
Define the work, user and operating boundary before choosing a model or framework.
- Workflow and stakeholder discovery
- Agent task, autonomy and approval boundaries
- Success measures and representative scenarios
Context and knowledge
Give the system the right information without turning every source into uncontrolled context.
- Instructions, state and context management
- Retrieval, grounding and data-quality decisions
- Freshness, permissions and source traceability
Tools and integration
Connect useful action with explicit contracts, permissions and failure handling.
- Function tools, APIs, connectors and MCP
- Authentication, authorization and least privilege
- Side effects, retries, idempotency and approval points
Orchestration and experience
Choose the simplest coordination pattern that supports the intended work.
- Single-agent, workflow and multi-agent trade-offs
- State, planning, delegation and handback
- Human intervention and user-experience design
Evaluation and safety
Make quality and risk visible through repeatable evidence.
- Scenario datasets, graders and regression tests
- Security, prompt injection and tool-use controls
- Tracing, observability and incident evidence
Production and handover
Ship with the technical and organisational pieces needed to operate the result.
- Deployment, scalability, latency and cost controls
- Monitoring, support and change-management practices
- Documentation, source ownership and knowledge transfer
Engagement brief
Scoped around risk, integration and ownership
A short discovery establishes the workflow, data, tools, identity, operational risk and handover expectations before delivery depth and timing are committed.
- Entry point
- New scenario, architecture need or existing prototype
- Audience
- Product, operations, engineering, platform and security teams
- Delivery
- Remote, hybrid or on-site according to the engagement
- Output
- Architecture, working system, production uplift or a scoped combination
- Handover
- Documentation and knowledge transfer agreed from the start
Practice-informed
Architecture depth without losing the operating reality
Fedorai founder Arafat Tehsin brings more than a decade of software delivery, solution architecture and applied AI experience. The work connects modern agent capability to the integration, security, evaluation and handover decisions that determine whether a system lasts.
About Arafat and Fedorai Questions
What organisations ask before starting
What kinds of AI agents does Fedorai build?
Engagements can cover knowledge and research agents, internal workflow agents, customer or employee assistants, tool-using operational agents and multi-step agent workflows. We confirm the task, users, data and action boundary before recommending an architecture.
Does Fedorai work with only one model provider or framework?
No. Technology choices are made against the scenario, existing estate, security requirements, team capability and operating constraints. The engagement may use OpenAI, Microsoft, open-source frameworks or an appropriate combination.
Can you help with an existing agent prototype?
Yes. A focused review can identify architecture, evaluation, security, integration, observability and production-readiness gaps, then define or implement the highest-priority improvements.
Can the agent integrate with our existing systems?
Integration is a core part of the architecture where the use case requires action or current business data. Feasibility depends on the available APIs, identity model, permissions, data quality and acceptable operational risk.
Will our team own the resulting solution?
Source ownership, hosting, documentation, support and handover are agreed during scoping. Fedorai engagements are designed to transfer understanding into the client team rather than leave an opaque system behind.
How long does an AI agent development engagement take?
Timing depends on the scenario, integrations, data readiness, security review and production expectations. A discovery or architecture review can be short and focused; a production build is estimated after the operating boundary is understood.