Discovery
From $750
one-time · USD
Workflow assessment, architecture decisions and an implementation plan.
Agentic AI Development Services for Real Business Operations
Custom agentic AI development services: RAG, tool calling, MCP integrations and evaluated workflows. Build, deploy and operate AI agents with LONGWApps.
Request a project proposalGive an agent a useful, bounded job.
An agent should move a real piece of work forward: classify an inquiry, retrieve the right policy, prepare an order update or assemble a case for review. We connect that work to your systems, define what the agent may do and give your team a way to evaluate the result.
- Request
- Evidence
- Agent
- Tool request
- Approval
- Action
The agent interprets the request, retrieves approved information and prepares a system update. A person approves consequential changes before the action proceeds.
Engineering beyond
the prompt.
RAG grounded in your knowledge.
Retrieval-augmented generation brings relevant business information into a response. We design ingestion, document chunking, metadata and retrieval around your sources. Access filters apply before information reaches the model; responses reference their evidence and escalate when the available material is insufficient.
- Source ownership, refresh rules and document permissions.
- Retrieval tests for relevance, freshness and citation quality.
Tool calling with explicit permissions.
Custom AI agent development turns model requests into controlled application actions. We define typed inputs and validate them in code, enforce authorization at the service boundary and handle retries, timeouts and duplicate requests. A model can propose an action; your business rules decide whether it can run.
- Scoped tools, validated arguments and idempotent writes.
- Human approval for payments, destructive edits and consequential changes.
MCP integration services.
Model Context Protocol connects agent applications to tools and contextual data through a common interface. We scope server selection or implementation, tool schemas, capability checks and authentication. We keep business authorization in the connected service and test what each agent can actually access.
- Supported MCP servers or custom adapters for your systems.
- Documented permissions, compatibility checks and failure handling.
Multi-agent orchestration.
Multi-agent AI development can separate retrieval, execution and verification when those responsibilities benefit from distinct tools or permissions. We define structured handoffs, shared state and a clear coordinator. Task deadlines, iteration limits and recovery paths keep a failed handoff from becoming an open-ended loop.
- Use specialist agents only when they improve the task.
- Compare quality, latency and cost with a simpler single-agent design.
Security at every boundary.
Prompts are not an access-control system. We treat user input, retrieved documents and tool responses as untrusted data, then enforce permissions outside the model. The design covers tenant boundaries, secrets, data minimization and approval gates, with adversarial tests for prompt injection and unauthorized tool use.
- Least-privilege credentials and environment-specific secrets.
- Data-flow review, redacted logs and agreed retention rules.
Observability from request to outcome.
A useful execution trace connects the original request to retrieved sources, model and prompt versions, tool calls, approvals and the final result. We instrument step duration, failures and usage cost so your team can investigate a bad answer, a slow integration or a rising bill without guessing.
- Correlation IDs, actionable alerts and cost thresholds.
- Sensitive-data redaction and clear ownership for exceptions.
Evaluation before every release.
We build an evaluation set from representative tasks, difficult edge cases and adversarial inputs. Tests cover task completion, evidence quality, tool selection, permissions, latency and cost. Acceptance thresholds are agreed with your team, and regression checks run before prompt, model or retrieval changes reach production.
- Repeatable test cases, scored results and human review.
- Release gates based on the complete workflow, not a polished demo.
Enterprise deployment & operations.
Enterprise AI agent development starts with your identity, data and operating requirements. We plan environment separation, integration access and versioned deployments, with staged rollout and rollback procedures. The deployment model is selected around data constraints, latency, operating cost and the people responsible for running it.
- Production configuration, recovery steps and a deployment runbook.
- Monitoring, incident handling and change review through managed operations.
From discovery to production.
Each stage produces something your team can inspect. The prototype proves the task; integration and evaluation establish whether it is ready to become part of daily operations.
Discovery
Map the workflow, users, data and baseline effort. Agree the outcome, boundaries and acceptance criteria.
Architecture
Select the model, retrieval approach, tools, state management and approval points. Record the important tradeoffs.
Prototype
Test a narrow task with representative examples, using read-only access or draft outputs where practical.
Integration
Connect business applications, enforce permissions and implement retries, duplicate protection and human handoffs.
Evaluation
Run the acceptance suite, adversarial cases and cost/latency checks. Resolve the failures that matter to the workflow.
Production
Release in stages, validate monitoring and recovery, and walk your team through the operating process.
Support
Review exceptions, maintain integrations and re-evaluate changes as your knowledge, models and business evolve.
A system your team can run.
The agreed handover covers the implementation and the evidence behind it, so your team can operate the system and understand the next change.
- Architecture and data-flow documentation, with tool and permission boundaries.
- Application source, agent instructions and integration configuration under the agreed ownership and licensing terms.
- An evaluation pack: representative cases, acceptance criteria and test results.
- Deployment configuration, monitoring guidance, recovery procedures and an operating walkthrough.
A practical investment. A defined scope.
Discovery starts from $750. A bounded agent implementation starts from $3,000 per scoped project, with managed operations from $750 per month. Enterprise, multi-agent and complex integration projects receive a tailored proposal. Model usage, hosting and third-party licenses are budgeted separately.
Agent implementation
From $3,000
per scoped project · USD
A defined agent workflow, connected and evaluated with your team.
Managed operations
From $750
per month · USD
Monitoring, maintenance and technical follow-through after launch.
Plan around the work.
These are indicative planning ranges, starting when scope and required access are agreed. We confirm the schedule with your team after reviewing data readiness, integrations and stakeholder review cycles.
- Discovery
- 3–5 working days for a focused workflow assessment.
- Bounded prototype
- 2–4 weeks to test a defined task with representative data.
- First production workflow
- 4–8 weeks for a narrow implementation with supported integrations.
- Enterprise or multi-agent scope
- 8–16+ weeks, planned around identity, data and integration requirements.
Before we build.
What is included in your agentic AI development services?
We scope the business task, design the architecture, build and connect the agent, and evaluate it against agreed acceptance criteria. The proposal identifies the source, configuration, evaluation evidence, deployment documentation and operating handover you receive.
Is an agent always the right answer?
No. A stable rules-based task may be easier to test and operate with conventional automation. We recommend an agent when interpreting variable information or choosing among approved tools adds useful value.
Can you build on our existing systems?
We start by reviewing your APIs, data sources, identity controls and integration permissions. Supported interfaces can be connected directly or through an adapter or MCP server. The architecture identifies the access and integration work required before implementation.
When does a multi-agent architecture make sense?
When distinct responsibilities need different knowledge, tools or permissions and structured handoffs improve the outcome. We compare it with a single agent or a deterministic workflow, including the extra latency, operating cost and failure paths.
How do people stay in control?
Permissions and business rules are enforced in the application and connected services. Your team approves consequential actions, reviews exceptions and can inspect execution traces. We define who can stop, change and restart the workflow.
What does an AI agent project cost?
A bounded implementation starts from $3,000. Discovery starts from $750 and managed operations from $750 per month. Your proposal sets out the workflow, integrations, evaluation requirements and total budget; model, hosting and third-party charges are separate.