Deployment sprint
Deploy one AI workflow into production in 2-4 weeks.
AgentLed pairs your team with an implementation engineer to turn one expensive manual process into a monitored workflow: connected to your tools, grounded in your business data, gated by human review, and improved from real runs.
AgentLed workspace
First sprint shape
Workflow
One repeated process with clear inputs, outputs, owners, edge cases, and acceptance criteria.
Workspace
Connections, memory, reusable skills, approval rules, monitoring, and credit visibility installed in AgentLed.
Launch
Live runs reviewed with your team, with misses turned into better rules, prompts, memory, and checks.
What this replaces
Not a generic automation setup. Not a strategy deck.
The point is to get a working system into production without forcing your team to become AI workflow engineers. The first sprint stays narrow enough to ship, but leaves behind a client workspace that makes the next workflow cheaper and easier.
Process discovery
We map the manual workflow, decision points, examples, failure cases, acceptance criteria, and what the owner would trust.
Workspace memory
The workflow can use your sources, files, CRM records, notes, rubrics, prior decisions, and structured workspace memory.
Implementation included
An engineer handles the build, integrations, prompt tuning, testing, and handoff instead of leaving you with a blank builder.
Human control
Sensitive outputs stop in an approval queue before they update records, send messages, publish content, or move work forward.
Context engineering
Build a behavioral operating envelope around the model.
An FDE does more than tune a prompt. They shape the context, permissions, checks, and feedback loop that make useful behavior more likely and make departures visible before they become business mistakes.
This is a behavioral operating envelope, not a literal fence around an LLM's latent space. Generation stays probabilistic; the deployment combines constraints, evaluation, monitoring, and human judgment to manage that reality.
Approved context and examples
Representative inputs, accepted outputs, policies, and counterexamples establish the quality bar for the workflow.
Retrieval of current knowledge
Product facts, customer context, brand guidance, and operational records are retrieved when the run needs them.
Instructions and permissions
Structured outputs, bounded tools, and least-privilege access constrain what the agent can propose or do.
Evaluations
Checks score correctness, tone, supported claims, safety, and drift against real examples before release.
Durable memory
Accepted edits, rejected outputs, exceptions, and outcomes become reusable context for later runs.
Monitoring and approval
Execution traces expose departures, while consequential actions stop for accountable human review.
The first deployment sprint
01
Choose the workflow
Pick one expensive, repeated, judgment-heavy process with examples, a clear owner, and visible ROI.
02
Install the workspace
Connect sources, define memory, add reusable skills, set approval rules, and establish the ROI baseline.
03
Run the first path
Test against past examples, then move to live runs with monitoring, human review, and escalation paths.
04
Productize the learning
Turn rejections and edge cases into reusable templates, rules, prompts, memory, and checks.
Start with one workflow. Leave with an operating layer.
A good first sprint is narrow, visible, and painful enough that saving time is obvious. The durable asset is the workspace your team can keep using after launch.