Service
Build, test, deploy and monitor AI agents as a repeatable practice.
ConnectSync helps companies set up the agent development lifecycle (ADLC), LangChain’s framework for building, testing, deploying and monitoring agents. We build agents on Deep Agents, LangChain’s open source agent harness, and use LangSmith to test, deploy and monitor them. We also advise your team on how to run the lifecycle.
Set up the full loop for an agent your team is building or already runs. We define the agent’s instructions, tools, skills, memory and subagents on Deep Agents. Then we add evaluations, deployment and tracing, and a routine for turning failures seen in production into fixes and tests.
Review how your team builds and ships agents today, and find where quality or risk is unclear. Decide what to adopt first, where your agents should run, and what to share across teams. You receive a written plan your team can carry out.
Keep agents affordable, controlled and easy to find as their number grows. Track what each agent spends on model calls, and set alerts and limits. Decide which tools each agent can use and on whose behalf, require human approval before sensitive actions, and keep a trace of every tool call for review. Keep one record of which agents exist, and share proven skills and prompts so teams don’t rebuild them.
Deep Agents is LangChain’s harness for complex, multi-step work. It uses the same tool-calling loop as other agent frameworks and adds what an agent needs to finish long tasks reliably:
The agent calls your tools and MCP servers, and reads and writes files. It can also run code in an isolated sandbox.
Skills load only when a task needs them, and memory carries preferences across conversations. Long histories are summarized so work can continue past one context window.
The agent hands subtasks to subagents that work in their own context and return only the result. A task list is available for long jobs.
The agent pauses for approval before the tool calls you mark as sensitive. Permission rules set which files it can read or write, and middleware adds your own rules around every model and tool call.
Deep Agents works with any model that supports tool calling, from providers such as Anthropic, OpenAI and Google. You can host an agent on your own infrastructure or run it in a LangSmith Deployment.
Not every agent needs a harness. For a simpler agent we use LangChain’s basic agent loop, and for a custom workflow we use LangGraph. Deep Agents is built on both.
Governance
Define the agent’s model, instructions, tools, skills, memory and subagents. Set approval points and file permissions from the start.
Start with a small dataset of representative tasks. Score each version of the agent with code-based checks and model-graded evaluators, and check the model’s grades against human review. Compare versions before each release, and test multi-turn agents with simulated conversations.
Run the agent in a LangSmith Deployment or on your own infrastructure. Each run saves its state at every step, so it resumes after a failure or a pause for approval. Each user’s conversations, memory and credentials can be kept private. Prompts and skills can be versioned separately from code, so your team can update them without a full release.
Trace what the agent and its subagents did. Track cost, latency and user feedback, score live traffic, and set alerts. LangSmith Engine can group recurring failures and propose fixes.
The steps form a loop. Production traces show where the agent fails, those cases become tests, and each fix is checked before it ships. Lessons from production also go back into the agent’s memory, skills and instructions, and your team reviews changes to anything shared.
Review the agent’s purpose, your current tools, data and access constraints, and who will own the agent.
Put the lifecycle in place for one representative agent, using the agreed tools and hosting.
Document the workflow and train your team. Agree who reviews evaluation results, handles incidents and approves changes after launch.
Tell us what your agent should do, what you have built so far and where it needs to run. We’ll use that to scope the first step together.