Agentic AI Development
Build custom autonomous software agents capable of independent multi-step planning, tool-calling execution, and contextual self-correction. These systems break down complex business objectives into isolated programmatic tasks without manual human intervention.
What we focus on
From strategy to launch, we help you build AI & Agents solutions that are reliable, scalable, and ready for the future.
- Multi-step process automation
- Autonomous research
- Complex customer service
CrewAI
LangGraph
OpenAI Assistants API
Claude
Google Gemini
LlamaIndex
Temporal
Qdrant
Milvus
Prefect
AWS Bedrock
MongoDBWe build systems that scale.
Agentic AI development is the engineering of autonomous software agents that plan multi-step tasks, call external tools and APIs to act on them, and adjust their own approach when a step fails or returns unexpected output. XOVO builds on a choice of four orchestration frameworks, LangGraph, CrewAI, AutoGen, or the OpenAI Assistants API, picked to match the workload instead of forcing one architecture onto every job. An agent runs a control loop that decides what to do next based on the current state of a workflow, retrying a failed tool call or falling back to an alternate one instead of simply stopping. Long-running workflows are wrapped in a durable execution engine, Temporal or Prefect, so a run can pause for a human approval and resume from the exact step it left off after a crash. Every plan, tool call, and output is traced, so when an agent does something unexpected you can replay the run and see which step caused it.
Everything in one place
- A working agent (or multi-agent crew) implemented in your chosen framework (typically LangGraph, CrewAI, AutoGen, or the OpenAI Assistants API), with the orchestration graph, roles, and handoff logic documented
- A catalog of tool/function definitions wiring the agent to your real systems: REST APIs, internal databases, search, and SaaS connectors, each with input/output schemas and error handling
- A retrieval layer over your knowledge sources using LlamaIndex for ingestion and Qdrant as the vector store, including the chunking and indexing pipeline
- A durable execution setup in Temporal or Prefect for long-running or scheduled agent runs, with retry, timeout, and resume behavior configured
- An evaluation and observability harness: a task test suite, per-step tracing, token/cost logging, and dashboards so you can see why an agent took a given action
- Guardrail and human-in-the-loop configuration covering tool-call approval gates, output validation, and the prompts, configs, and infrastructure-as-code needed to run it all yourself
Ready to get started?
Book a free scoping call and we'll map Agentic AI Development to your workflow.
- A 30-minute call with a senior engineer, not a salesperson
- A tailored rollout plan scoped to your existing stack
- A straight answer on timeline and cost, no pressure to commit
A clear process. Predictable results.
Task and tool mapping
We map the target workflow into discrete steps and identify which actions the agent must take versus which need a human. For each action we list the system it touches (API, database, SaaS app, internal search) and define the tool interface and the data the agent needs to read or write.
Orchestration design
We choose the framework and model the workflow explicitly: a LangGraph state graph with decision and retry edges, a CrewAI/AutoGen crew with defined roles and handoffs, or Assistants API threads. We also design the retrieval layer in LlamaIndex and Qdrant if the agent needs to ground decisions in your documents or data.
Tool and agent build
We implement the tool/function definitions, wire them to your real endpoints with schema validation and error handling, and build the agent loop. Long-running or scheduled flows are wrapped in Temporal or Prefect so runs are durable, can pause for approvals, and resume after interruptions.
Evaluation and guardrails
We assemble a test suite of representative tasks and run the agent against them, measuring task completion, tool-call correctness, and cost per run. We add guardrails (output validation, tool-call approval gates, and human-in-the-loop checkpoints for high-stakes actions) and tune prompts and graph logic against the eval results rather than ad hoc.
Deployment and observability
We deploy with per-step tracing, token and cost logging, and dashboards so you can watch live runs and inspect any decision. We hand over the code, configs, and infrastructure-as-code, and tune the system on real traffic, adjusting tools, prompts, and routing as edge cases surface.
Task and tool mapping
We map the target workflow into discrete steps and identify which actions the agent must take versus which need a human. For each action we list the system it touches (API, database, SaaS app, internal search) and define the tool interface and the data the agent needs to read or write.
Orchestration design
We choose the framework and model the workflow explicitly: a LangGraph state graph with decision and retry edges, a CrewAI/AutoGen crew with defined roles and handoffs, or Assistants API threads. We also design the retrieval layer in LlamaIndex and Qdrant if the agent needs to ground decisions in your documents or data.
Tool and agent build
We implement the tool/function definitions, wire them to your real endpoints with schema validation and error handling, and build the agent loop. Long-running or scheduled flows are wrapped in Temporal or Prefect so runs are durable, can pause for approvals, and resume after interruptions.
Evaluation and guardrails
We assemble a test suite of representative tasks and run the agent against them, measuring task completion, tool-call correctness, and cost per run. We add guardrails (output validation, tool-call approval gates, and human-in-the-loop checkpoints for high-stakes actions) and tune prompts and graph logic against the eval results rather than ad hoc.
Deployment and observability
We deploy with per-step tracing, token and cost logging, and dashboards so you can watch live runs and inspect any decision. We hand over the code, configs, and infrastructure-as-code, and tune the system on real traffic, adjusting tools, prompts, and routing as edge cases surface.
Outcomes you can count on.
Concrete gains Agentic AI Development delivers for your operations, measured in hours saved, errors removed, and cost avoided, not vague promises. Each outcome below maps to a specific capability you can trace end-to-end, so the impact is visible from day one.
Workflows that run end-to-end, not step-by-step
Instead of a person shepherding a task through ten tools, the agent plans the sequence, calls each tool, and only stops to ask a human when a decision genuinely needs sign-off. This is useful for multi-step process automation and autonomous research that would otherwise tie up a queue of staff.
Recovery built into the control loop
Because each step is an explicit node with defined failure paths, an agent can re-read an API error, retry with corrected arguments, or fall back to an alternate tool instead of silently failing, and durable execution means a crash or restart resumes the run rather than losing it.
Auditable decisions you can debug
Every plan, tool call, and intermediate output is traced and logged, so when an agent does something unexpected you can replay the exact run, see which step caused it, and fix that node, rather than re-prompting blindly and hoping.
A framework choice matched to the problem
We pick the orchestration approach to fit the workload: LangGraph for tightly controlled state and branching, CrewAI or AutoGen for collaborating specialist agents, the Assistants API where managed tool execution is enough, so you are not forced into one architecture for every use case.
