01
Define good work
Set task-specific criteria and choose representative examples.
The platform
One operating layer. Three ways to stay in charge.
Agentabl is being built around observability, governance, and portability—supported by evaluation, optimization, and the everyday work of operating agents.
Request a Demo01 / ObservabilityProduct direction
Know how they’re doing.
Understand the work behind the result. Start with activity and performance, then investigate the executions, model calls, tool actions, and exceptions that need attention.
- Agent inventory and ownership
- See which agents are in scope, what they are responsible for, who owns them, and where they run.
- Execution visibility
- Follow recorded inputs, outputs, steps, model calls, and tool actions through a run.
- Cost and performance
- Compare usage, estimated model costs, response times, and failure patterns where instrumentation makes them available.
- Quality and attention
- Bring task-level evaluations, review findings, and exceptions into the operating picture.
Cross-framework visibility is product direction. Recorded evidence is not access to private model reasoning; estimated model spend is not total business-task cost.
Agentabl/WorkspaceAgent operations
Last 24 hours120Total runs
110Completed
6Failed
4Awaiting approval
Recent runs3 of 120
Agent / ownerLatest runEst. model cost / duration
Support Reply AssistantCustomer Operations
Waiting for approval
Est. model cost $0.06Duration Waiting
Research AssistantProduct Operations
Completed
Est. model cost $0.09Duration 6.2 s
Customer Record AgentRevenue Operations
Failed
Est. model cost $0.02Duration 1.8 s
A completed run does not automatically mean good work. Quality needs its own criteria and review.
02 / Governance
Decide how they work.
Give agents a clear operating scope. Define what they may access, what actions they may take, and which situations need human review.
- Access and permissions
- Scope access to models, tools, knowledge, memory, and credentials.
- Guardrails and operating limits
- Apply input/output checks, allowed-tool rules, business rules, and resource limits at supported execution points.
- Risk-based review
- Match oversight to the action’s consequence. Use pre-action approval and post-run quality review for their different purposes.
- Human intervention
- Give reviewers the requested action, relevant context, and available decisions—not just an alert.
- Audit evidence
- Record applicable policy checks, human decisions, and execution outcomes so teams can investigate what happened.
Available controls depend on the runtime and integration. Activity visibility alone does not provide authority to block or pause an external agent.
Agentabl/WorkspaceReview before sending
Waiting for approvalSupport Reply AssistantAction · Send a customer-facing answer
Review reason
The draft includes a policy statement without a supporting source.
Applied rule: Review required when the policy statement lacks a source.Draft excerpt
“Your plan includes priority support outside business hours.”Available evidence
The retrieved plan document confirms business-hours support but does not establish outside-hours coverage.
Suggested next step: request a revision grounded in the available plan document.
Review what needs approval before it happens. Review completed work to improve what happens next.
03 / PortabilityProduct direction
Keep your options open.
Your choice of model or framework should be a decision you can revisit. Make compatibility, required adaptations, and evaluation evidence visible before a change.
- Model and provider choice
- Compare compatible alternatives against your task criteria, latency expectations, and cost constraints.
- Reusable agent assets
- Reuse compatible prompts, tool definitions, workflow assets, and evaluation cases where the source and destination support them.
- Framework migration
- Identify what can transfer, what needs adapting, and what must be retested—including moves into and out of Agentabl.
- Environment changes
- Assess deployment requirements and dependencies when planning a move between supported environments.
Connecting systems is different from moving assets. Prompts, tool behavior, permissions, memory, and runtime state may need different handling; neither path guarantees identical behavior everywhere.
Agentabl/WorkspaceCompare before changing models
Support-answer checks, version 1 · 40 sample cases
Review target: at least 90% of cases pass.
Criterion: source support and answer completeness.
Current
Model A
90%36/40 passing review- Est. cost / task
- $0.06
- Median time
- 4.8 s
Candidate
Model B
92.5%37/40 passing review- Est. cost / task
- $0.04
- Median time
- 3.6 s
Candidate
Model C
85%34/40 passing review- Est. cost / task
- $0.03
- Median time
- 3.0 s
The lowest-cost option doesn’t meet this example’s quality target.
Illustrative results and prices—not provider pricing or a product benchmark.
Compare first. Adapt deliberately. Validate before switching.
Evaluation & optimizationProduct direction
Turn review into improvement.
Define what good work looks like. Review results, find weaknesses, and test changes to prompts, models, and context. Compare quality, speed, and cost before putting changes into use.
02
Review the results
Combine evaluation results with human feedback and production exceptions.
03
Test the improvement
Compare alternatives against the same criteria before deciding what to change.
Evaluation
Test task success, accuracy where reference answers exist, groundedness, output requirements, and relevant safety or bias criteria. Keep the method and test set visible.
Optimization
Test prompts, context, retrieval, models, and operating configuration. Reviewed failures and recurring exceptions help strengthen the next test set.
Evaluation and optimization support every part of Agentabl—not just model switching.
Agent operationsThe practical foundation
The practical foundation
behind the oversight.
Connect agent definitions and resources with execution, review, and change management.
Inventory and lifecycle
Manage agent identity, ownership, resources, and lifecycle state.
Execution and recovery
Work with supported triggers, schedules, run status, retries, and recovery paths.
Knowledge and memory
Attach the information and context an agent is allowed to use.
Native building tools
Create workflow-backed agents with the visual builder and SDK when that is the right path.
The builder is one way to create agents. The operating layer is the product’s organizing idea.
Do we have to rebuild our agents in Agentabl?
That is not the intended model. The external-agent direction is to connect existing agents through supported integrations. The specific connection and control options need to be confirmed for your stack.
Can we use Agentabl to build new agents?
The development foundation includes a native workflow builder, agent management, reusable resources, and an SDK. A demo can establish the paths available for your use case.
Does switching a model guarantee the same results?
No. Compare the proposed model against representative tasks and check its compatibility with your prompts, tools, and output requirements before changing it.
Can Agentabl control every action an external agent takes?
Only actions covered by the supported control integration can be governed through that integration. Visibility, invocation, action controls, and runtime management are different levels of support.
A clearer view starts here
Request a DemoBetter oversight. More room to move.
Tell us what your agents do, where they run, and what you need to improve.