Agents that plan, use tools, check their own work, and keep going until the job is actually done. We build the ones that run in production, not the ones that work in a demo.
Start a projectA chatbot answers questions. An agent does the job. It reads your data, calls your APIs, makes decisions, handles errors, and loops until the task is complete or it knows to escalate. The difference between these two things is about six months of engineering.
We build agents that know what they can do and what they cannot. Every agent has a cost ceiling so a runaway loop does not cost you $400 at 3am. Every agent has an escalation path so edge cases go to a human instead of getting a hallucinated answer. The result is software that runs unattended, not a demo you babysit.
One agent, one job. Research, data extraction, code generation, document processing. It gets a task, picks its tools, does the work, and verifies the output before returning it. No multi-agent complexity when a single well-built agent handles the job.
When the task is too complex for one agent, we split it. A researcher gathers data, a writer drafts the output, a reviewer checks it. Each agent has its own context window and toolset. Handoffs are explicit, not implicit. The orchestrator tracks what is done and what is left.
Agents are only useful if they can do things. We connect them to your databases, APIs, file systems, and internal tools through MCP. Every tool call is logged, every result is validated, and the agent knows what each tool does before it tries to use it.
Your agent talks to external agents via A2A protocol. It discovers what other agents can do, delegates work, and collects results. Standardized message formats so agents from different systems can actually collaborate instead of just being in the same architecture diagram.
The agent handles the 80% it is confident about. The 20% that needs judgment gets routed to a human with full context attached. Confidence scoring on every decision, configurable thresholds, and approval queues for anything high-stakes.
You need to know if your agent is working. Task completion rates, cost per run, latency, and output quality tracked in real time. When we change a prompt or swap a model, automated eval catches regressions before users do.
Define what the agent should do, which tools it needs, what good output looks like, and what happens when things go wrong. Build test cases from real examples.
Implement the agent with tool definitions, memory, and error handling. Test against real scenarios. A working version runs in your environment by end of week one.
Add cost ceilings, timeouts, and escalation paths. Run adversarial inputs. Find the edge cases the happy path never shows.
Deploy with observability. Track what the agent does, how much it costs, and how often it succeeds. Eval runs on every change.
Deployed agent system running in your infrastructure with defined task boundaries, tool access, and error recovery. Not a notebook.
MCP server setup, API connections, and custom tools. Every external system the agent uses is documented, tested, and versioned.
Automated tests covering normal use, edge cases, and adversarial inputs. Runs on every prompt or model change.
Token usage, cost per task, latency, and success rates in real time. Alerts when something looks wrong.
What the agent does, what it cannot do, how to maintain it, and what to do when it breaks. Written for engineers, not marketing.