From co-pilotto autopilot.
Most companies use AI to answer questions faster. We build agents that take the task, plan the steps, call your tools, check their own work, and hand back a finished outcome.
Watch an agentrun the brief.
This is the difference in practice. The same request goes into both. A chatbot hands back a paragraph and waits. An agent comes back when the work is shipped.
We are not automating tasks. We are automating roles.
The run above is an illustrative replay of our revenue-analyst pattern. The shape is always the same: the agent plans the steps, operates your real systems, checks its own output, and delivers where your team already works. Nothing routes through a chat window unless you want it to.
OAuth-scoped connectors, SQL through audited credentials, webhooks for everything event-driven. All inside your accounts, under your access controls.
A loop,not a leap.
An agent is trustworthy because of what wraps the model, not the model alone. Every task runs the same loop, and the loop is what we engineer.
The agent decomposes the goal into ordered steps, picks the tools each step needs, and writes the plan down before touching anything.
When a step fails, the loop retries it, reroutes it, or raises a hand. Failure is a branch we design for, not a crash you discover.
Eight roles,ready to hire.
Eight agent classes already shipped to production, built on LangGraph, CrewAI, and the Claude Agent SDK. Open a file: each one takes a discrete role, or runs alongside the team that owns it today.
L1 support agent
Reads the ticket, diagnoses the root cause against your knowledge base, applies the fix, and closes the loop with the customer.
Each file is a pattern we have shipped, not a slide. You pick the role; we scope the agent to your stack, your policies, and your definition of done.
Trust is shippedin stages.
An agent earns autonomy the way a new hire does: by proving itself. Across twelve weeks the human share of the work shrinks, and it only shrinks when the evals say it should.
Scope and architecture
- Shadow the workflow and capture the decision rules and edge cases
- Allow-list the tools and systems the agent is permitted to touch
- Define done, and the eval criteria that will gate the ship
Build the loop
- Wire the tools: the APIs, databases, and queues the role needs
- Build the plan, act, check loop with retries and recovery paths
- Grow the eval harness from real cases your team supplies
Supervised runs
- The agent does the work; a human approves every consequential action
- Observability lands: traces, costs, and a replayable audit log
- Pass the gates: 95%+ task completion, 99%+ safety compliance
Production
- Full runs inside guardrails; exceptions escalate to a human
- Every action logged with rule, actor, timestamp, and outcome
- The next workflow queues up while this one keeps working
By week nine the agent is doing the work under supervision. By week twelve, the only steps it waits on are the ones you chose to gate.
Every actionpasses a gate.
Autonomy is only useful if it cannot surprise you. Every action the agent takes is checked against the rule set before it executes, below the model, where it cannot be argued with.
The agent can only touch what its role permits. Everything else does not exist to it.
Deletes, drops, and truncates are blocked below the model. Policy is not a prompt.
Transfers, exports, and access changes hold for an explicit human approval. You set the thresholds.
Everything it ships is checked against retrieved context. Confidence is never a source.
Rule, actor, timestamp, and outcome on every decision. Any run replays in seconds.
The AI does 99% of the work. You press the final button.
One week inside the workflow. Out comes the scope, the eval criteria, and one fixed number.
Twelve weeks to production, priced by the outcome. No hourly meter, no per-seat fees.
It runs in your VPC on your LLM spend. Code, prompts, evals, and runbooks are yours.
Tell us which workflow you want off your team’s plate; we send back a fixed number.
One agent,zero knowledge lost.
The agency that stopped losing what it knew.
Every time an assistant left, six months of client context walked out with them. We built a continuity agent that captures meetings, monitors Slack, and writes SOPs in real time, so the knowledge stays even when the people change.
We went from losing weeks of productivity every time a VA churned to having new team members productive on day one. The agent doesn't just document, it understands context.
Common questions about autonomous agents.
What teams ask before they hand real work to an agent.
Twelve weeks from kick-off to production. Week 1-2: scoping and architecture. Week 3-8: agent development, tool integration, eval harness. Week 9-12: production hardening, observability, handover.
BearPlex prices by outcome, not hours. A one-week discovery produces the scope, the eval criteria, and a fixed quote for the whole build. The number depends on how many tools the agent touches, how many systems it integrates with, and the eval rigor your domain demands. The agent runs in your cloud on your LLM spend, so there is no per-seat meter. Tell us the workflow and we will scope it.
LangGraph (our default for stateful workflows), CrewAI for multi-agent orchestration, the Claude Agent SDK for Anthropic deployments, LangChain for simpler chains, and native function-calling for tight integrations. We pick based on your stack, not vendor affinity.
Always yours. BearPlex's sovereign deployment model means the agent runs in your VPC, on your LLM provider of choice (OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, or on-prem Llama/Mistral). We hand over full source code and runbooks at engagement end.
Every BearPlex agent ships with an evaluation harness: golden task datasets, LLM-as-judge scoring, regression tests, and observability (OpenTelemetry + LangSmith or Arize). We target 95%+ task completion and 99%+ safety compliance before production cutover.
Some work shouldrun itself.
Pick the workflow that eats your team's week. We will scope the agent, build it inside your guardrails, and hand you the keys.